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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 309)
Control Systems     Hybrid Journal   (Followers: 249)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 195)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 191)
Electronics     Open Access   (Followers: 131)
Advances in Electronics     Open Access   (Followers: 126)
Electronic Design     Partially Free   (Followers: 125)
Electronics For You     Partially Free   (Followers: 124)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 116)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 92)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 88)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 88)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 84)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 84)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 81)
IET Power Electronics     Open Access   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 67)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 63)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 62)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 58)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 53)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 52)
Advances in Power Electronics     Open Access   (Followers: 49)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 45)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 41)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 35)
IET Microwaves, Antennas & Propagation     Open Access   (Followers: 34)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 33)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 29)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 28)
Electronics Letters     Open Access   (Followers: 28)
Microelectronics and Solid State Electronics     Open Access   (Followers: 27)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 27)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 24)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
Journal of Sensors     Open Access   (Followers: 23)
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 22)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 20)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 18)
Journal of Artificial Intelligence     Open Access   (Followers: 18)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 17)
IET Wireless Sensor Systems     Open Access   (Followers: 17)
Circuits and Systems     Open Access   (Followers: 16)
Machine Learning with Applications     Full-text available via subscription   (Followers: 16)
Archives of Electrical Engineering     Open Access   (Followers: 15)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
International Journal of Control     Hybrid Journal   (Followers: 14)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
Superconductivity     Full-text available via subscription   (Followers: 13)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 13)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 12)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 12)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 11)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 11)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 11)
IETE Journal of Research     Open Access   (Followers: 10)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 10)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 10)
Open Journal of Antennas and Propagation     Open Access   (Followers: 10)
Nature Electronics     Hybrid Journal   (Followers: 9)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 9)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 9)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 8)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
China Communications     Full-text available via subscription   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 8)
Batteries     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 8)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 7)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 7)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 7)
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Nanotechnology, Science and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 7)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Electronic Markets     Hybrid Journal   (Followers: 6)
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 6)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
Journal of Power Electronics     Hybrid Journal   (Followers: 6)
Energy Storage Materials     Full-text available via subscription   (Followers: 6)
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access   (Followers: 6)
Journal of Optoelectronics Engineering     Open Access   (Followers: 5)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Field Robotics     Hybrid Journal   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Wireless and Mobile Technologies     Open Access   (Followers: 4)
Frontiers in Electronics     Open Access   (Followers: 4)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 4)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 4)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 4)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 4)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 4)
Advanced Materials Technologies     Hybrid Journal   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal   (Followers: 4)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 3)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
e-Prime : Advances in Electrical Engineering, Electronics and Energy     Open Access   (Followers: 3)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Advancing Microelectronics     Hybrid Journal   (Followers: 3)
IETE Journal of Education     Open Access   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 3)
Sensors International     Open Access   (Followers: 3)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 3)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 3)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
ACS Applied Electronic Materials     Open Access   (Followers: 2)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal   (Followers: 2)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 2)
Energy Storage     Hybrid Journal   (Followers: 2)
Journal of Information and Telecommunication     Open Access   (Followers: 2)
Transactions on Electrical and Electronic Materials     Hybrid Journal   (Followers: 2)
IET Smart Grid     Open Access   (Followers: 2)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 2)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Nuclear Cardiology     Hybrid Journal   (Followers: 2)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 1)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 1)
IEEE Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 1)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Електротехніка і Електромеханіка     Open Access   (Followers: 1)
Ural Radio Engineering Journal     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
International Journal of Hybrid Intelligence     Hybrid Journal   (Followers: 1)
Open Electrical & Electronic Engineering Journal     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 1)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Automatika : Journal for Control, Measurement, Electronics, Computing and Communications     Open Access  
npj Flexible Electronics     Open Access  
Elektronika ir Elektortechnika     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access  
IEEE Solid-State Circuits Letters     Hybrid Journal  
IEEE Open Journal of Industry Applications     Open Access  
IEEE Open Journal of the Industrial Electronics Society     Open Access  
IEEE Open Journal of Circuits and Systems     Open Access  
Journal of Electronic Science and Technology     Open Access  
Solid State Electronics Letters     Open Access  
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Journal of Engineered Fibers and Fabrics     Open Access  
Jurnal Teknologi Elektro     Open Access  
IET Nanodielectrics     Open Access  
Elkha : Jurnal Teknik Elektro     Open Access  
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Jurnal Teknik Elektro     Open Access  
IACR Transactions on Symmetric Cryptology     Open Access  
Acta Electronica Malaysia     Open Access  
Bioelectronics in Medicine     Hybrid Journal  
Chinese Journal of Electronics     Open Access  
Problemy Peredachi Informatsii     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access  
Telematique     Open Access  
International Journal of Nanoscience     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
Semiconductors and Semimetals     Full-text available via subscription  

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Similar Journals
Journal Cover
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 63  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [228 journals]
  • [Front cover]

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      Abstract: Presents the front cover for this issue of the publication.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • IEEE Geoscience and Remote Sensing Society

    • Free pre-print version: Loading...

      Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Information for Authors

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      Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SAR Image Despeckling Using Continuous Attention Module

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      Authors: Jaekyun Ko;Sanghwan Lee;
      Pages: 3 - 19
      Abstract: Speckle removal process is inevitable in the restoration of synthetic aperture radar (SAR) images. Several variant methods have been proposed for enhancing SAR images over the past decades. However, in recent studies, convolutional neural networks (CNNs) have been widely applied in SAR image despeckling because of their versatility in representation learning. Nonetheless, a fair number of textures of the images are still lost when despeckling using simple CNN structures. To solve this problem, an encoder–decoder architecture was previously proposed. Although this architecture extracts features on different scales and has been shown to yield state-of-the-art performance, it still learns representation locally, resulting in missing overall information of convolutional features. Therefore, we herein introduce a new method for SAR image despeckling (SAR-CAM), which improves the performance of an encoder–decoder CNN architecture by using various attention modules. Moreover, a context block is introduced at the minimum scale to capture multiscale information. The model is trained via a data-driven approach using the gradient descent algorithm with a combination of modified despeckling gain and total variation loss function. Experiments performed on simulated and real SAR data demonstrate that the proposed method achieves significant improvements over state-of-the-art methodologies.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Spatial-Aware Hyperspectral Nonlinear Unmixing Autoencoder With Endmember
           Number Estimation

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      Authors: Kazi Tanzeem Shahid;Ioannis D. Schizas;
      Pages: 20 - 41
      Abstract: In this article, we develop a novel fully unsupervised autoencoder-based scheme for nonlinear hyperspectral pixel unmixing. A unique approach is derived where high noise and unresponsive pixels are accounted for, by a unique averaging approach based on spatially aware filters built using radial basis function (RBF) kernels. A novel technique is implemented via calculating rank-equivalent kernel covariance matrices in order to estimate the unknown number of endmembers contributing to the data. Utilization of spatial information is done via RBF-based weighted averaging, which is then followed by endmember estimation via K-means clustering. The RBF distances from the cluster centers are determined to measure the position of the mixed pixels in relation to the centers, which is utilized as a preliminary estimation of the abundances. The proposed framework is robust in the presence of unresponsive pixels, while highly versatile working with different nonlinear unmixing models. Extensive numerical tests establish the superiority of the novel approach with respect to state-of-the-art methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimation and Validation Study of Soil Moisture Using GPS-IR Technique
           

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      Authors: G. N. Madhavi;P. Sharath Kumar;R. A. Chipade;Jyoti Bhate;Tummalapalli Venkata Chandrasekhar Sarma;
      Pages: 42 - 49
      Abstract: Soil Moisture (SM) data play an important role in different fields of research like hydrology, agriculture, climatology, etc. In this article, global positioning system interferometric reflectometry technique was used to estimate SM. Estimated SM data have been validated and compared with collocated in situ probe, Soil Moisture and Ocean Salinity (SMOS) satellite measurements, ECMWF ERA-5, and NASA Global Land Data Assimilation System (GLDAS); the former shows a good correlation of 0.98 but the magnitudes of SMOS, ERA-5 are overestimated and GLDAS data are underestimated. Variability of SM with rainfall and energy fluxes like latent and sensible heat fluxes from ERA-5 and GLDAS data are investigated. Observed SM values are positively correlated with rainfall during the study period. Seasonal variations of SM with rainfall in different monsoons are clearly noticed. Latent heat fluxes are more during spring, summer months and positively correlated with rainfall, whereas sensitive heat fluxes show negative correlation.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Analyzing the Saturation of Growing Stem Volume Based on ZY-3 Stereo and
           Multispectral Images in Planted Coniferous Forest

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      Authors: Tingchen Zhang;Hui Lin;Jiangping Long;Meng Zhang;Zhaohua Liu;
      Pages: 50 - 61
      Abstract: Recently, remote sensing (RS) technology are becoming an increasingly important technology in estimating forest growing stem volume (GSV), and the saturation issue of spectral variables from various optical sensors severely hinders the improvement of mapping forest GSV, especially in the planted forest with high GSV. Forest canopy height is widely considered as one of the major factors to increase the saturation levels in mapping GSV. However, it is rather difficult to invert the forest canopy height without precisely external DEM for large regions. In this study, the canopy height model (CHM) was derived from ZY-3 stereo images with subtracting open-sourced external DEM and the response of saturation levels was analyzed by adding forest height in the planted coniferous forest (Larch and Chinese pine). To further describe the relationships between the forest height and saturation levels, five datasets with five estimation models (Linear, MLR, SVR, KNN, and RF) and three methods of variable selection (Stepwise, LASSO, and Pearson) were applied to estimate the forest GSV using corrected CHM and 49 alternative variables extracted from ZY-3 multispectral images. Meanwhile, a spherical model was employed to quantitatively describe the saturation levels of combined variables. The results showed that values of relative root means square error were decreased from 29.3% to 25% for Larch and from 26.5% to 22.2% for Chinese pine after adding the corrected CHM, respectively. Meanwhile, the saturation level of each combined variable set was successfully determined by the spherical model. The results illustrated that the saturation levels of GSV were significantly increased by adding corrected CHM from open-sourced external DEM. Specially, the averaged saturation levels were increased from 220 m3/ha to nearly 300 m3/ha for Chinese pine and from -50 m3/ha to 220 m3/ha for Larch, respectively. It is proved that ZY-3 stereo and multispectral images have great potential for accurate estimation of forest GSV by delaying the saturation levels using extracted CHM with open-sourced external DEM.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • PRISMA Spatial Resolution Enhancement by Fusion With Sentinel-2 Data

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      Authors: Nicola Acito;Marco Diani;Giovanni Corsini;
      Pages: 62 - 79
      Abstract: This article deals with the problem of improving the spatial resolution of hyperspectral (HS) data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, 10 S2 bands at 10 and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The article presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low-resolution PRISMA image, provides a super-resolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA HS sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to coregister the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved HS image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Temporal Variography for the Evaluation of Atmospheric Carbon Dioxide
           Monitoring

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      Authors: David Torres;Norman Toro;Edelmira Gálvez;David Castillo;Sebastián Arenas Bermúdez;Alessandro Navarra;
      Pages: 80 - 88
      Abstract: Since 1958, the Mauna Loa Observatory (MLO) has continuously monitored carbon dioxide variations using nondispersive infrared sensors, with the Keeling curve as an early indicator of the anthropogenic contribution to global atmospheric carbon dioxide. The increasing CO2 levels are alarming and have led to international agreements that promote cleaner industrial activities. However, any change in global behavior would not immediately cause detectable changes in the MLO data; the extent to which global and long-term trends are conflated with local and short-term variations remains unclear. Hence the current article verifies the performance of the sampling and measurement systems of MLO, using existing data published within the months of October and November 2020, which comply with the temporal continuity requirements of chronostatistics. It has been determined that the components of the MLO air including carbon dioxide are well mixed due to their particular location. Beyond this, the variographic analysis distinguishes between small (10%) variability contributions due to sampling, including graphical depictions of MLO data. Coupled with the precision of the method being better than 0.2 ppm, it has been determined that the sampling and measurement protocols are highly suitable to meet the objective of representing CO2 fluctuations over time. The variographic application also manages to quantify short-term variabilities resulting from the local processes of the region where the observatory is located. The results support the furthering of multiscaled temporal analysis of atmospheric CO2, and potentially the incorporation of CO2 variographic parameters into empirical and semiempirical climate models.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Global Shallow Groundwater Patterns From Soil Moisture Satellite
           Retrievals

    • Free pre-print version: Loading...

      Authors: Mehmet Evren Soylu;Rafael L. Bras;
      Pages: 89 - 101
      Abstract: Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015–2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Regularized Dual-Channel Algorithm for the Retrieval of Soil Moisture and
           Vegetation Optical Depth From SMAP Measurements

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      Authors: Julian Chaubell;Simon Yueh;R. Scott Dunbar;Andreas Colliander;Dara Entekhabi;Steven K. Chan;Fan Chen;Xiaolan Xu;Rajat Bindlish;Peggy O'Neill;Jun Asanuma;Aaron A. Berg;David D. Bosch;Todd Caldwell;Michael H. Cosh;Chandra Holifield Collins;Karsten H. Jensen;Jose Martínez-Fernández;Mark Seyfried;Patrick J. Starks;Zhongbo Su;Marc Thibeault;Jeffrey P. Walker;
      Pages: 102 - 114
      Abstract: In August 2020, soil moisture active passive (SMAP) released a new version of its soil moisture and vegetation optical depth (VOD) retrieval products. In this article, we review the methodology followed by the SMAP regularized dual-channel retrieval algorithm. We show that the new implementation generates SM retrievals that not only satisfy the SMAP accuracy requirements, but also show a performance comparable to the single-channel algorithm that uses the V polarized brightness temperature. Due to a lack of in situ measurements we cannot evaluate the accuracy of the VOD. In this article, we show analyses with the intention of providing an understanding of the VOD product. We compare the VOD results with those from SMOS. We also study the relation of the SMAP VOD with two vegetation parameters: tree height and biomass.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar
           Activity and Magnetic Storm Periods

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      Authors: Yi Han;Lei Wang;Wenju Fu;Haitao Zhou;Tao Li;Ruizhi Chen;
      Pages: 115 - 126
      Abstract: Precise ionospheric total electron content (TEC) is critical for many aerospace applications, and forecasting ionospheric TEC is of great significance to it. Besides, short-term prediction of TEC values fills the gap between the TEC product latency and the precision. The machine learning-based approaches are promising in solving the nonlinear prediction issues, particularly suitable for short-term global positioning system TEC forecasting due to its complex temporal and spatial variation. In this article, four different machine learning models, i.e., artificial neural network, long short-term memory networks, adaptive neuro-fuzzy inference system based on subtractive clustering, and gradient boosting decision tree (GBDT) are applied for forecasting ionospheric TEC in three IGS GNSS monitoring stations at the low-latitude region (16°S to 10°S). The performance of these approaches in extreme conditions is investigated, including the high solar activity and magnetic storm, which are the most challenging scenario for TEC prediction. The results show that the machine learning algorithms outperform the global ionospheric map prediction model. The prediction accuracy during the high solar activity period was improved from 37.93% to 49.28%. During the magnetic storm period, the prediction accuracy was improved from 28.16% to 67.39%. Among the machine learning algorithms, the GBDT model outperforms the rest three algorithms in ionosphere prediction scenarios, which improves the prediction accuracy by 5.6% and 12.7% than the rest three approaches on average during high solar activity (2012–2015) and magnetic storm periods respectively.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Kinematic Behavior Analysis of the Wadi Landslide From Time-Series
           Sentinel-1 Data

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      Authors: Mi Jiang;Xia Zhao;Xuguo Shi;
      Pages: 127 - 135
      Abstract: The analysis of kinematic characteristics and triggering factors of the potentially unstable slopes is of great significance for the slope protection and landslide reinforcing. Located in Mao Country, Sichuan Province in China, Wadi village is a typical mountainous area where geological disasters frequently occur due to the complex geological environment, intense tectonic activities, and concentrated seasonal rainfall. However, the slope stability and potential risk over this area are not fully evaluated yet. In this article, we investigate the unstable slope in Wadi village using time-series synthetic aperture radar interferometry (InSAR) technique. The deformation history retrieved from 132 C-band descending Sentinel-1 images between October 2014 and September 2019 demonstrates that the Wadi landslide presents a slow sliding trend with the averaged line-of-sight velocity of −70 mm/year and has a distinct seasonal velocity at the head and toe area. We further decompose the predominant and periodic velocities by a kinematic model to analyses spatial and temporal characteristics of Wadi landslide. The results reveal that the Wadi landslide is a hybrid type of push and pull with two source areas in the head and toe of the slope, respectively. The periodic velocity variations are highly correlated with seasonal rainfall. Our study also demonstrates the importance of correcting InSAR unwrapping error for interpreting the landslide kinematics and other small-scale geomorphological processes.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Sea Ice Detection and Measurement Using Coastal GNSS Reflectometry:
           Analysis and Demonstration

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      Authors: Feng Wang;Dongkai Yang;Mingjie Niu;Lei Yang;Bo Zhang;
      Pages: 136 - 149
      Abstract: Based on a developed three-layerair–ice–water reflection model, this article simulates the evolution of reflection coefficient versus elevation angle. Due to the interference between the signal components from the air–ice and ice–water interfaces, the reflection coefficient experiences an oscillating pattern versus elevation angle so that detecting sea ice using the power or amplitude of the reflected global navigation satellite system (GNSS) signal has to choose a suitable satellite to reduce the influence of the oscillating pattern. A sea ice surface is more stable and presents higher correlation than a dynamic ocean surface, this article explores the potential of detecting and measuring sea ice using the coherency of reflected GNSS signal for coastal scenario. Experimental results show that phase coherency can significantly detect sea ice without strictly limiting elevation and azimuth angle. This article also is to is to explore the potential of retrieving sea ice thickness using the oscillating phase pattern versus elevation angle. The phase compensation and the dual-polarization observation are proposed to remove the delay phase between the direct and reflected signal from the estimated phase of the reflected GNSS signal. The results show that the amplitude and frequency of the oscillating phase pattern, respectively, have an inversely proportional and positively linear relationship with sea ice thickness. Simulation shows that, compared to the oscillating amplitude, the oscillating frequency is a better choice to measure sea ice thickness. The frequency of the dual-polarization oscillating pattern could provide the measurement performance with a root-mean-square error of 0.05 m.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Adversarial Learning Based Discriminative Domain Adaptation for Geospatial
           Image Analysis

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      Authors: Nikhil Makkar;Lexie Yang;Saurabh Prasad;
      Pages: 150 - 162
      Abstract: The ability of supervised image analysis methods to provide state-of-the-art performance is limited by availability of high-quality labeled data in large quantities. Domain adaptation approaches propose a solution to this problem by leveraging quality labeled information from auxiliary data sources. In this work, we use adversarial learning for domain adaptation for remote sensing applications. First, we approached the problem of unavailable target domain labels with unsupervised domain adaptation and then extended our method for semisupervised domain adaptation to use a few available labels as well. We are using adversarial learning to extract discriminative target domain features that are aligned with source domain. We test our framework for two very different applications of remote sensing imagery, multiclass classification in hyperspectral images and semantic segmentation in large scale satellite images. For hyperspectral image analysis two datasets were used: the University of Houston shadow data was used for quantifying the efficacy of our approach to varying illumination, and the Botswana data was used to quantify the efficacy of our approach under multitemporal spectral shifts. Multisensor high-resolution images from National Agriculture Imagery Program and SpaceNet-Rio datasets were used as the source and target for the task of building extraction for large scale semantic segmentation based domain adaptation.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

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      Authors: Ning Zhang;Lin Sun;Zhendong Sun;
      Pages: 163 - 172
      Abstract: The GF-4 satellite has a moderate spatial resolution and is capable of high temporal frequency observation. Thus, it can play an important role in the acquisition of information regarding fires. In this article, the characteristics of brightness temperature difference between fire point area and nonfire point area are comprehensively analyzed. Employing the advantages of high-frequency continuous observation of these data, an improved algorithm for joint spatial attribute detection using multitemporal data is proposed. This method is based on the traditional contextual method and incorporates the concept of time sequence, and uses the changes in time phase before and after the fire to reduce the false alarm caused by the high heterogeneity of pixels in a single spatial scene in the traditional method. In order to evaluate the accuracy of the algorithm, the perfect moderate resolution imaging spectrometer thermal anomaly product is used as the verification data to evaluate the feasibility of the algorithm. At the same time, it is necessary to compare the algorithm in this article with the fire algorithm currently applied to GF-4 with the four typical regions. The improved algorithm shows better drawing ability, and the consistency of fire description of biomass combustion for many days is improved. In the case of the Heilongjiang region on October 30, 2017, the accuracy rate is improved by 62%, compared with the contextual method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Preliminary Studies on CIMR Antenna Pattern Brightness Temperature
           Compensation

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      Authors: Marco Brogioni;Ada Vittoria Bosisio;Alessandro Lapini;Giovanni Macelloni;Giuseppe Addamo;Giuseppe Virone;Walter Di Nicolantonio;Marco Grilli;Oscar A. Peverini;
      Pages: 173 - 183
      Abstract: Spaceborne microwave radiometry provides an essential contribution to monitoring the Earth with varying spatial resolution both related to the reflector dimension and the frequency of operation. The ESA's Copernicus imaging microwave radiometer (CIMR) mission aims at collecting the geophysical observables at a spatial resolution ranging from 60 km in L band to 4 km in Ka band. This goal can be achieved by equipping CIMR with a large unfurlable mesh reflector antenna. A limitation of the antenna design is that the antenna pattern includes grating lobes that contaminate the scene measurement with contributions originated far from the nominal footprint. This effect introduces inaccuracies in brightness temperature measurements, particularly when facing radiometric discontinuities, e.g., near the coastlines and sea ice edges, which can be greater than the mission required maximum of 0.5 K. The aim of this article is to assess a technique which will be able to correct the effects of antenna pattern and obtain reliable TB measurements. The analyzed simple technique is based on a regularized deconvolution of the antenna pattern to reconstruct the actual brightness temperatures. The technique was tested over a synthetic scenario that mimics both steep and smooth variations in spatial and thermal domains.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Classification via Global-Local Hierarchical Weighting
           Fusion Network

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      Authors: Bing Tu;Wangquan He;Wei He;Xianfeng Ou;Antonio Plaza;
      Pages: 184 - 200
      Abstract: The fusion of spectral–spatial features based on deep learning has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral–spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the fusion process, which is not conducive to improving the discrimination of spectral–spatial features. This article proposes a global–local hierarchical weighted fusion end-to-end classification architecture. The architecture includes two subnetworks for spectral classification and spatial classification. For the spectral subnetwork, two band-grouping strategies are designed, and bidirectional long short-term memory is used to capture spectral context information from global to local perspectives. For the spatial subnetwork, a pooling strategy based on local attention is combined to construct a global–local pooling fusion module to enhance the discriminability of spatial features learned by a convolutional neural network. For the fusion stage, a hierarchical weighting fusion mechanism is developed to obtain the nonlinear relationship between both spectral and spatial features. The experimental results on four real HSI datasets and a GF-5 satellite dataset demonstrate that the method proposed is more competitive in terms of accuracy and generalization.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Quantifying Land Use/Land Cover Change and Urban Expansion in Dongguan,
           China, From 1987 to 2020

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      Authors: Peng Dou;Zhen Han;
      Pages: 201 - 209
      Abstract: Dongguan has experienced the most rapid urbanization since the Chinese reform and opening policy. To analyze the urban expansion in this city, in this study, 12 land use/land cover maps were produced using a multiple classifiers system on the Google Earth Engine platform. A long time-series land use dataset from 1987 to 2020, was achieved. The results indicate that, during the past 33 years, the urban area increased by 13.84 times and reached 1483.06 km2 in 2020, whereas cultivated land and forest continually decreased because of rapid urbanization. By analyzing the changes in urban forms and landscape indexes, Dongguan experienced a diffusion–coalescence–diffusion phase, and its urban expansion can be divided into three stages: The early-age urbanization stage (1987–1996), the rapid urbanization stage (1996–2011), and the intensive urbanization stage (2011–2020). The development of the economic and dual-level administration of Dongguan expanded the urban area surrounding the towns and traffic corridors, and, finally, formed a connected urban area. The urban expansion, just like sparking spots firing the prairie, is special and conforms to a Dongguan urbanization pattern.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Remote Sensing Systems for Ocean: A Review (Part 1: Passive Systems)

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      Authors: Meisam Amani;Arsalan Ghorbanian;Milad Asgarimehr;Bahareh Yekkehkhany;Armin Moghimi;Shuanggen Jin;Amin Naboureh;Farzane Mohseni;Sahel Mahdavi;Nasir Farsad Layegh;
      Pages: 210 - 234
      Abstract: Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, remote sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this article, the goal is to review most RS systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, thermal infrared radiometers, microwave radiometers, and Global Navigation Satellite Systems, are comprehensively discussed. Additionally, this article summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An APMLP Deep Learning Model for Bathymetry Retrieval Using Adjacent
           Pixels

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      Authors: Jinshan Zhu;Jian Qin;Fei Yin;Zhaoyu Ren;Jiawei Qi;Jingyu Zhang;Ruifu Wang;
      Pages: 235 - 246
      Abstract: Shallowwater depth plays an important role in marine development, navigation safety, and environmental protection. It is an efficient and economical way to obtain water depth by remote sensing technology. At present, most empirical models based on multispectral image usually obtain water depth by the relationship between the sea surface reflectance (SSR) (a single pixel) and in situ water depth, it is a one-to-one correspondence between the reflectance and depth. However, seafloor substrate and inherent optical properties (IOP) will also have contribution to the SSR. In this article, we propose an adjacent pixels multilayer perceptron model (APMLP) model using adjacent pixels to weaken the influence of seafloor substrate and IOP.Datasets on Oahu Island (Sentinel-2B, LIDAR in situ data) and Saint Thomas Island (Sentinel-2A, LIDAR in situ data) are used to establish and verify the model. The APMLP model are also compared with the multilayer perceptron model (MLP) model, BP neural network model, and Log-ratio model. The overall root-mean-square error (RMSE) of APMLP model on Oahu Island is 0.72 m, which is much better than the other three models (MLP 1.07 m, BP 1.05 m, Log-ratio 1.52 m). Similar results are obtained from the Saint Thomas Island dataset, RMSE of APMLP model is 1.56 m, better than the other three (MLP 1.91 m, BP 1.89 m, Log-ratio 2.39 m). The study confirms that considering adjacent pixels in an artificial neural network model can effectively improve the performance of water depth retrieval.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a
           Rainfall–Runoff Model in the U.S. Corn Belt

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      Authors: Navid Jadidoleslam;Brian K. Hornbuckle;Witold F. Krajewski;Ricardo Mantilla;Michael H. Cosh;
      Pages: 247 - 260
      Abstract: L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from in situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Ionospheric–Thermospheric Responses in South America to the August 2018
           Geomagnetic Storm Based on Multiple Observations

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      Authors: Munawar Shah;Ayesha Abbas;Muhsan Ehsan;Andres Calabia Aiber;Binod Adhikari;M. Arslan Tariq;Junaid Ahmed;José Francisco de Oliveira-Júnior;Jianguo Yan;Angela Melgarejo-Morales;Punyawi Jamjareegulgarn;
      Pages: 261 - 269
      Abstract: The ionospheric storm time responses during August 2018 are investigated over South American region using multiple observables, for example, Global Navigation Satellite System (GNSS) derived vertical total electron content (VTEC) from International GNSS Service, magnetic field data, geomagnetic indices, global ionospheric maps, thermospheric mass density (TMD), and [O/N2] ratio measurement. Strong-ionospheric and upper-atmospheric disturbances affected the ionospheric variables with long duration during the storm recovery phase and following after. First, daytime VTEC (9:00–20:00 UT) presented variations of>15 TECU during days 25 to 30 of August 2018 in low and middle latitudes of South America, this after sudden storm commencement (SSC). Furthermore, nighttime (21:00–24:00 and 00:00–05:00 UT) VTEC presented low values (5
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Generalized Zero-Shot Domain Adaptation for Unsupervised Cross-Domain
           PolSAR Image Classification

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      Authors: Rong Gui;Xin Xu;Rui Yang;Kailiang Deng;Jun Hu;
      Pages: 270 - 283
      Abstract: Cross-domain polarimetric synthetic aperture radar interpretation is urgently needed, due to the rapid data growth and label scarcity. However, the class distribution shift problems hinder the reuse of labeled samples among cross-domain images. Most of the existing domain adaptations can only handle the cross-domain case of same categories between source and target domains, while the categories of the target domain are usually more abundant than those of the source domain. To improve the usability of labeled samples among cross-domain images, an unsupervised generalized zero-shot domain adaptation (uGZSDA) based on scattering component semantics (SCSs) is proposed. By using SCSs and limited labeled samples (seen categories) in the source domain, more land cover types (seen and unseen categories) in the unlabeled target domain can be inferred. First, a stacked autoencoder (SAE) extracts source/target-domain features, and SCSs of typical land covers are constructed by cross-domain databases and statistical scattering components. Second, combining SAE features and source-domain samples, the most likely seen class samples in the target domain are selected by probability sorting, and the SAE is retrained by obtained selected seen samples. Third, the unseen class samples in the target domain are inferred by the retrained SAE, classification probability, and semantic similarity. Finally, the selected seen and inferred unseen class samples in the target domain are used to further retrain the SAE, and the target domain is classified by the retrained SAE and the classifier. The proposed uGZSDA is verified among 16 cross-domain PolSAR datasets. Using SCS and two to three types of seen samples from the source domain, the accuracies of seven types of land covers in the unlabeled target domain can reach 76–83.96%.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Unsupervised Data Fusion With Deeper Perspective: A Novel Multisensor Deep
           Clustering Algorithm

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      Authors: Kasra Rafiezadeh Shahi;Pedram Ghamisi;Behnood Rasti;Paul Scheunders;Richard Gloaguen;
      Pages: 284 - 296
      Abstract: The ever-growing developments in technology to capture different types of image data [e.g., hyperspectral imaging and light detection and ranging (LiDAR)-derived digital surface model (DSM)], along with new processing techniques, have led to a rising interest in imaging applications for Earth observation. However, analyzing such datasets in parallel, remains a challenging task. In this article, we propose a multisensor deep clustering (MDC) algorithm for the joint processing of multisource imaging data. The architecture of MDC is inspired by autoencoder (AE)-based networks. The MDC paradigm includes three parallel networks, a spectral network using an autoencoder structure, a spatial network using a convolutional autoencoder structure, and lastly, a fusion network that reconstructs the concatenated image information from the concatenated latent features from the spatial and spectral network. The proposed algorithm combines the reconstruction losses obtained by the aforementioned networks to optimize the parameters (i.e., weights and bias) of all three networks simultaneously. To validate the performance of the proposed algorithm, we use two multisensor datasets from different applications (i.e., geological and rural sites) as benchmarks. The experimental results confirm the superiority of our proposed deep clustering algorithm compared to a number of state-of-the-art clustering algorithms. The code will be available at [Online]. Available: https://github.com/Kasra2020/MDC.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Evaluation of Crop Health Status With UAS Multispectral Imagery

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      Authors: Odysseas Vlachopoulos;Brigitte Leblon;Jinfei Wang;Ataollah Haddadi;Armand LaRocque;Greg Patterson;
      Pages: 297 - 308
      Abstract: This study presents the results of a field experiment conducted for assessing the crop health status of several barley and oat crop fields in Prince Edward Island, Canada. The crop fields were mapped with an unmanned aircraft system (UAS), and the crop health status was assessed through the green area index (GAI) and vegetation indices (VIs). GAI maps were produced from the UAS imagery and VIs used machine learning pipelines with several regression algorithms (multiple linear models, support vector machines, random forests, and artificial neural networks) along with a feature selection strategy. The random forests algorithm was shown to be the best algorithm for GAI prediction with an average relative root mean square error of 10.86% and a mean absolute error of 0.67. The resulting GAI maps and the regression feature space were classified with random forests to discriminate between vigorous and stressed crop areas. We achieved a mean overall accuracy of 94%. The limits of the study are also presented.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Nighttime Vitality and Its Relationship to Urban Diversity: An Exploratory
           Analysis in Shenzhen, China

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      Authors: Junwei Zhang;Xintao Liu;Xiaoyue Tan;Tao Jia;Ahmad M. Senousi;Jianwei Huang;Ling Yin;Fan Zhang;
      Pages: 309 - 322
      Abstract: Relationship between urban diversity and urban vitality is imperative for guiding better design in urban development, although existing frameworks are not able to efficiently examine the relationship at multiple scales. In this article, we propose a new framework to integrate nighttime light (NTL) imagery and multisource urban data into multiscale geographically weighted regression (MGWR) models to examine the varying relationship between diversity and vitality across space and time. NTL is used as a proxy for urban nighttime vitality. Public transport, taxi transit, and points of interest data are used to derive three aspects of urban diversity indices: ridership diversity, spatial interaction diversity, and built environment diversity. By comparing the models in holiday and nonholiday weeks in Shenzhen, China, the NTL-based vitality proxy was found to be strongly correlated with the urban diversity indices, given by the satisfactory goodness of fit (r-squared = 0.9) of the MGWR models. The spatially varying relationships between diversity indices and nighttime vitality were observed and patterns discussed. The analysis of the coefficients revealed the importance of stable public transport and fluctuating taxi trips for nighttime vitality. The new index proposed for the diversity of spatial interaction (DSI) is a strong indicator for nighttime vitality, adding to existing vitality indicators. Furthermore, this study found that DSI and density of catering have less temporal variation, indicating their robustness in measuring nighttime vitality. This study provided empirical insights into how nighttime vitality is related to urban diversity, demonstrating new applications of NTL for intracity studies.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An Empirical Study of the Convolution Neural Networks Based Detection on
           Object With Ambiguous Boundary in Remote Sensing Imagery—A Case of
           Potential Loess Landslide

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      Authors: Guangle Yao;Wenlong Zhou;Mingzhe Liu;Qiang Xu;Honghui Wang;Jun Li;Yuanzhen Ju;
      Pages: 323 - 338
      Abstract: Many objects in the naturalenvironment are generated from the background and even transformed by nature or human beings. Thus, they do not have closed and well-defined boundaries in remote sensing imagery. Recently, convolutional neural network (CNN) based object detection achieved great success in the remote sensing field. However, there is no investigation in the literature about the detection of objects with ambiguous boundaries. In this article, taking the case of the potential loess landslide detection, we designed massive experiments to evaluate convolutional neural networks for detecting objects with ambiguous boundaries in remote sensing imagery. We analyzed the evaluated methods comprehensively by comparing the performance on objects with ambiguous boundaries in remote sensing imagery with the performance on ordinary objects in visual imagery. Furthermore, drawing from these analyses, we provided a fundamental principle of object representation and a meaningful suggestion of information learning to detect objects with ambiguous boundaries. We finished this article by presenting several promising directions for detecting objects with ambiguous boundaries to facilitate and spur future research. This article would provide a significant reference and guidance to develop detectors for objects with ambiguous boundaries in remote sensing imagery.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • CSF-Net: Color Spectrum Fusion Network for Semantic Labeling of Airborne
           Laser Scanning Point Cloud

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      Authors: Jihao Li;Wenkai Zhang;Wenhui Diao;Yingchao Feng;Xian Sun;Kun Fu;
      Pages: 339 - 352
      Abstract: Airborne laser scanning point cloud semantic labeling, which aims to identify the category of each point, plays a significant role in many applications, such as forest observing, powerline extraction, etc. Under the guidance of deep learning technology, the interpretation thought of point clouds has also greatly changed. However, owing to the irregular and unordered natures of point clouds, it is relatively difficult for classification model to distinguish some objects with similar geometry by single-modal data only. Fortunately, additional gain information, e.g., color spectrum which can be complementary to geometric information, is able to effectively promote the classification effect. Therefore, the design of fusion strategy is a critical part in model construction. In this article, aiming to capture more abstract semantic information for color spectrum data, we elaborate a color spectrum fusion (CSF) module. It can be flexibly integrated into a classification pipeline with just negligible parameters. Then, we expand data fusion thoughts for point clouds and color spectrum and investigate three possible fusion strategies. Accordingly, we develop three architectures to construct CSF-Nets. Ultimately, by taking a weighted cross entropy loss, we can train our CSF-Nets in an end-to-end manner. Experiments on two extensively used datasets: Vaihingen 3D and LASDU show that the presented three fusion approaches all can improve the performance, while the earlier fusion strategy performs the best. Besides, compared with other well-performed methods, CSF-Net is still able to achieve satisfactory performance on overall accuracy and m$F_{1}$-score indicator. This also validates the effectiveness of our multimodal fusion network.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimation and Spatiotemporal Variation Analysis of Net Primary
           Productivity in the Upper Luanhe River Basin in China From 2001 to 2017
           Combining With a Downscaling Method

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      Authors: Qinru Liu;Liang Zhao;Rui Sun;Tao Yu;Shun Cheng;Mengjia Wang;Anran Zhu;Qi Li;
      Pages: 353 - 363
      Abstract: The upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental health. Although several vegetation productivity products have been generated, poor spatiotemporal resolution limits their application in ecosystem service assessment. In this article, vegetation net primary productivity (NPP) from 2000 to 2017 with a resolution of 30 m in the upper Luanhe River Basin was generated based on a data fusion model and the multisource data synergized quantitative (MuSyQ) NPP model. Then, the variation trend of NPP and its climate controls were analyzed. Compared with forest NPP observation data, we derived an R2 of 0.68 and the root-mean-square error of 81.70 gC.m−2.yr−1. Annual NPP had a fluctuating increasing trend from 2001 to 2017, with values ranging between 3.43 and 5.00 TgC.yr−1, with an annual increase trend of 0.04 TgC.yr−1. Precipitation was significantly correlated with NPP in the upper part of the Luanhe River basin, which is an important reason for the interannual variation of NPP. Grassland had a stronger correlation to precipitation than forest because it is more sensitive to precipitation. The area where the temperature is significantly correlated with annual NPP only accounts for 2% of the study area, indicating that temperature has a weak influence on NPP. Furthermore, human activities, such as forest management, fertilization, and irrigation, can change the trend of annual NPP.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Validation of Soil Moisture Data Products From the NASA SMAP Mission

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      Authors: Andreas Colliander;Rolf H. Reichle;Wade T. Crow;Michael H. Cosh;Fan Chen;Steven Chan;Narendra Narayan Das;Rajat Bindlish;Julian Chaubell;Seungbum Kim;Qing Liu;Peggy E. O'Neill;R. Scott Dunbar;Land B. Dang;John S. Kimball;Thomas J. Jackson;Hala Khalid Al-Jassar;Jun Asanuma;Bimal K. Bhattacharya;Aaron A. Berg;David D. Bosch;Laura Bourgeau-Chavez;Todd Caldwell;Jean-Christophe Calvet;Chandra Holifield Collins;Karsten H. Jensen;Stan Livingston;Ernesto Lopez-Baeza;José Martínez-Fernández;Heather McNairn;Mahta Moghaddam;Carsten Montzka;Claudia Notarnicola;Thierry Pellarin;Isabella Greimeister-Pfeil;Jouni Pulliainen;Judith Gpe. Ramos Hernández;Mark Seyfried;Patrick J. Starks;Zhongbo Su;R. van der Velde;Yijian Zeng;Marc Thibeault;Mariette Vreugdenhil;Jeffrey P. Walker;Mehrez Zribi;Dara Entekhabi;Simon H. Yueh;
      Pages: 364 - 392
      Abstract: The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-mean-square error
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted
           Low-Rank Representation for Hyperspectral Image Unmixing

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      Authors: Hongjun Su;Cailing Jia;Pan Zheng;Qian Du;
      Pages: 393 - 408
      Abstract: Sparse unmixing with a semisupervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit the performance of sparse unmixing. In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted low-rank representation unmixing (SBWCRLRU). In this method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes the rank of the abundance matrix to promote the spatial consistency of the image. Meanwhile, superpixel segmentation is adopted to cluster the pixels into different spatial homogeneous regions to further improve the unmixing performance. Extensive experiments results conducted on both synthetic and real data demonstrate the effectiveness of the proposed SBWCRLRU. It can not only improve the performance of hyperspectral unmixing but also outperform the existing sparse unmixing approaches.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SNLRUX++ for Building Extraction From High-Resolution Remote Sensing
           Images

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      Authors: Yanjing Lei;Jiamin Yu;Sixian Chan;Wei Wu;Xiaoying Liu;
      Pages: 409 - 421
      Abstract: Building extraction plays an important role in high-resolution remote sensing image processing, which can be used as the basis for urban planning and demographic analysis. In recent years, many powerful general semantic segmentation models have emerged, but these models often perform poorly when transferred to remote sensing images because of the characteristics of remote sensing images. To this end, we propose a new deep learning network called Selective Nonlocal ResUNeXt++ (SNLRUX++) for building extraction. First, the cascaded multiscale feature fusion is proposed to transform the high-performance image classification network ResNeXt into the segmentation network ResUNeXt++. Second, selective nonlocal operation is designed to establish long-range dependencies while avoiding introducing excessive noise and computational effort. Finally, multiscale prediction is applied as deep supervision to accelerate training and convergence, and improves prediction performance of objects at different scales. The experimental results on two different remote sensing image datasets show the effectiveness and generalization ability of the proposed method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Relation-Attention Networks for Remote Sensing Scene Classification

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      Authors: Xin Wang;Lin Duan;Chen Ning;Huiyu Zhou;
      Pages: 422 - 439
      Abstract: Remote sensing (RS) scene classification plays an important role in a wide range of RS applications. Recently, convolutional neural networks (CNNs) have been applied to the field of scene classification in RS images and achieved impressive performance. However, to classify RS scenes, most of the existing CNN methods either utilize the high-level features from the last convolutional layer of CNNs, missing much important information existing at the other levels, or directly fuse the features at different levels, bringing redundant and/or mutually exclusive information. Inspired by the attention mechanism of the human visual system, in this article, we explore a novel relation-attention model and design an end-to-end relation-attention network (RANet) to learn powerful feature representations of multiple levels to further improve the classification performance. First, we propose to extract convolutional features at different levels by pretrained CNNs. Second, a multiscale feature computation module is constructed to connect features at different levels and generate multiscale semantic features. Third, a novel relation-attention model is designed to focus on the critical features whilst avoiding the use of redundant and even distractive ones by exploiting the scale contextual information. Finally, the resulting relation-attention features are concatenated and fed into a softmax layer for the final classification. Experiments on four well-known RS scene classification datasets (UC-Merced, WHU-RS19, AID, and OPTIMAL-31) show that our method outperforms some state-of-the-art algorithms.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Evaluation and Validation of the Net Primary Productivity of the Zoigê
           Wetland Based on Grazing Coupled Remote Sensing Process Model

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      Authors: Li He;Chengying Li;Zhengwei He;Xian Liu;Rui Qu;
      Pages: 440 - 447
      Abstract: Zoigê wetland is located in the southeastern edge of the Qinghai–Tibet Plateau, in recent years, the wetland local area grassland productivity decline, land sanding, and other serious degradation phenomenon. Using remote sensing and hydrology data combined with the basic principles of vegetation ecology, we applied the Carnegie–Ames–Stanford Approach (CASA) model and Boreal Ecosystem Productivity Simulator (BEPS) model to invert the net primary productivity (NPP) of the Zoigê wetland ecosystem and then identified the deficiencies of the two models. In this article, we tested the factors that influence the light use efficiency of vegetation and considered the effects of livestock grazing on grassland ecosystems and wetland moisture content on vegetation growth. A new grazing coupled remote sensing process (GCRSP) model suitable for the productivity of the Zoigê wetland was proposed. Field survey data were combined with the CASA model, BEPS model, and Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product, and the accuracy of the proposed GCRSP model was analyzed at regional level. The results show that after fitting the field measurements to the GCRSP model, the fitted variance between the model and the measured data is 0.84, which is larger than the variance between the other two methods and the measured data. In addition, the variance of the linear fit between the GCRSP model and the MODIS NPP product was 0.11, which was relatively small, indicating that the regional-scale GCRSP model was relatively accurate for the Zoigê wetland NPP. Therefore, this model was suitable for the inversion of regional-scale NPP. The results indicated that the average grassland NPP of the Zoigê wetland was 487.57 gC/m2, and larger NPP values were distributed near the rivers and wetland and small-r NPP values were distributed in the mountain glacier and desertified land areas. These results were consistent with the regional characteristics. This article provided technical and theoretical support for the desertification management of the Zoigê grassland and a new method for studying vegetation NPP, and the findings could help improve the accuracy of regional-scale vegetation NPP estimates.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Feature Matching and Position Matching Between Optical and SAR With Local
           Deep Feature Descriptor

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      Authors: Yun Liao;Yide Di;Hao Zhou;Anran Li;Junhui Liu;Mingyu Lu;Qing Duan;
      Pages: 448 - 462
      Abstract: Image matching between the optical and synthetic aperture radar (SAR) is one of the most fundamental problems for earth observation. In recent years, many researchers have used hand-made descriptors with their expertise to find matches between optical and SAR images. However, due to the large nonlinear radiation difference between optical images and SAR images, the image matching becomes very difficult. To deal with the problems, the article proposes an efficient feature matching and position matching algorithm (MatchosNet) based on local deep feature descriptor. First, A new dataset is presented by collecting a large number of corresponding SAR images and optical images. Then a deep convolutional network with dense blocks and cross stage partial networks is designed to generate deep feature descriptors. Next, the hard L2 loss function and ARCpatch loss function are designed to improve matching effect. In addition, on the basis of feature matching, the two-dimensional (2-D) Gaussian function voting algorithm is designed to further match the position of optical images and SAR images of different sizes. Finally, a large number of quantitative experiments show that MatchosNet has a excellent matching effect in feature matching and position matching. The code will be released at: https://github.com/LiaoYun0x0/Feature-Matching-and-Position-Matching-between-Optical-and-SAR.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Orientated Silhouette Matching for Single-Shot Ship Instance Segmentation

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      Authors: Zhenhang Huang;Ruirui Li;
      Pages: 463 - 477
      Abstract: Object detection and semantic segmentation have achieved remarkable performance propelled by deep convolutional neural networks. However, neither of them can well parse and deal with swarms of rotating ships in remote sensing images. In this article, we pay more attention to the instance-level segmentation task, which recognizes objects more effectively and straightly. We propose a new network architecture, called orientated silhouette matching network, employing multiscale features and instance-level masks to enable single-shot and anchor-box-free instance segmentation. To be specific, we propose a novel-orientated polar template mask with orientated mask IoU to better match the ship silhouette. We also design a multiscale feature propagation and fusion module to improve the precision of detection. To further improve the performance, our network adopts Res2Net and Soft-NMS. Extensive experiments on the open datasets, namely Airbus Ship, demonstrate that our method improves the average precision by 14.2 and 10.0 percentage points on Res2Net101, compared with PolarMask and YOLACT. The source code will be open source after the reviewing process.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • The Relationship Between Urban 2-D/3-D Landscape Pattern and
           Nighttime Light Intensity

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      Authors: Bin Wu;Chengshu Yang;Zuoqi Chen;Qiusheng Wu;Siyi Yu;Congxiao Wang;Qiaoxuan Li;Jianping Wu;Bailang Yu;
      Pages: 478 - 489
      Abstract: As spatial and socioeconomic processes are the two key aspects of urban development, revealing the relationship between these two key aspects is critical. Previous studies attempted to explain their correlation at the city or region level using built-up area metrics and nighttime light (NTL) data. However, more comprehensive studies on urban interior spatial characteristics and their relationship to NTL intensity are lacking in a three-dimension space. Using Luojia 1-01 nighttime light data, LiDAR digital surface model data, and other auxiliary data, this study applies an extreme gradient boosting regression model and Sharpley Additive exPlanations method to model and interpret the relationship between two-dimensional (2-D)/3-D landscape patterns and NTL intensity. Two study areas were selected to investigate the landscape–NTL relationship at the parcel and subdistrict levels. The major findings of this study include the following: 1) 2-D and 3-D urban landscape patterns have a close relationship with NTL intensity at the parcel and subdistrict scales; 2) the combinational metric of 2-D and 3-D landscape patterns has a stronger relationship with NTL intensity than either the 2-D or 3-D landscape metrics alone; 3) the correlations between most landscape metrics and NTL intensity are not simply positive or negative but change as metrics grow; and 4) the urban socioeconomic level is not only related to a single landscape metric sometimes but tends to the result of metrics interaction. These findings may help urban planners and government officials make more reasonable urban landscape planning policies under the goal of sustainable development.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Long Time Series Water Extent Analysis for SDG 6.6.1 Based on the GEE
           Platform: A Case Study of Dongting Lake

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      Authors: Chunlin Wang;Weiguo Jiang;Yue Deng;Ziyan Ling;Yawen Deng;
      Pages: 490 - 503
      Abstract: Understanding the variation regularity of water extent can provide insights into lake conservation and management. In this study, inter- and inner-annual variations of water extent during the period of 1987–2020 were analyzed to understand the temporal and spatial distribution characteristics of Dongting Lake. We applied the Multiple Index Water Detection Rule to extract the Dongting Lake water extent quickly and accurately based on Google Earth Engine platform, and then assessed the extraction accuracy. The water surface analysis results showed that (1) based on sustainable development goals (SDG) 6.6.1, the trend of water extent showed the downward fluctuating trend from 1987 to 2020, with the overall average water extent being 1894.48 km². (2) Among the monthly average water area, the largest extent was 2477.14km² (July) and the smallest was 848.14 km² (January). Among the seasonal mean water area, summer was the largest, with an area of 2438.06 km², and winter was the smallest at 967.34 km². (3) For the water inundation frequency, seasonal water bodies accounted for the largest proportion, with 1577.85 km²; the nonwater area was the smallest, with the area of 573.02 km²; and the permanent water area was 1086.21 km². Through the analysis of the historical water body extent of the long time series of Dongting Lake, this study reflected support for SDG, for which the research idea and design can help us understand the importance and feasibility of the SDG 6.6.1 indicator.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Ground-Based Remote Sensing and Uncertainty Analysis of the Mass Eruption
           Rate Associated With the 3–5 December 2015 Paroxysms of Mt. Etna

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      Authors: Luigi Mereu;Simona Scollo;Costanza Bonadonna;Franck Donnadieu;Valentin Freret-Lorgeril;Frank S. Marzano;
      Pages: 504 - 518
      Abstract: During explosive eruptions, the real-time estimation of the mass eruption rate (MER) is challenging although crucial to mitigate the impact of erupted tephra. Microwave radar techniques at L- and/or X-bands, as well as thermal infrared imagery, can provide a reliable MER estimation in real time. Using lava fountains of 3–5 December 2015 at Mt. Etna (Italy) as test cases, we investigate the differences amongall these remote sensing methods and introduce a new approach, called the near source approach (NSA) using only X-band radar data. We also extend the volcanic advanced radar retrieval methodology to estimate the gas-tephra mixture density near the volcanic crater. The analysis of uncertainty is carried out comparing the NSA with the mass continuity approach (MCA), top plume approach (TPA) and surface flux approach (SFA), already used to estimate the MER of other Etna explosive events. The analysis allows us to identify the optimal real-time MER retrieval strategy, showing the potential and limitations of each method. We show that the MCA method, entirely based on the X-band radar data processing, is the best strategy with a percentage uncertainty in the MER estimation of 22.3%, whereas other approaches exhibit a higher uncertainty (26.4% for NSA, 30% for TPA, and 31.6% for SFA).
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Identifying Soil Freeze/Thaw States Using Scattering and Coherence Time
           Series of High-Resolution C-Band Synthetic Aperture Radar in the
           Qinghai-Tibet Plateau

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      Authors: Xin Zhou;Zhengjia Zhang;Qikai Shen;Qihao Chen;Xiuguo Liu;
      Pages: 519 - 532
      Abstract: The soil freeze/thaw (F/T) cycles play an important role in the climate system and human activities. However, the harsh environment in the Qinghai-Tibet Plateau (QTP) poses great challenges for both in-situ observation and remote-sensing monitoring of the soil F/T process. In this article, the time series of scattering and coherence of the high-resolution Sentinel-1 C-band synthetic aperture radar (SAR) is analyzed to identify the soil F/T state. The time series of scattering, including intensity and decomposition parameters, and coherence, are analyzed based on three typical landcover types (i.e., desert, grassland, and meadow) in the QTP. They are given the mathematical description by second-order and fourth-order Fourier functions, respectively. Based on Fourier functions, the initial F/T time points of the soil are detected in each pixel to draw the F/T map of the entire study area. The experiment results are cross-validated with the initial F/T time points of the soil calculated from the MODIS land surface temperatures, showing that the differences in days are less than one revisit cycle of Sentinel-1 (i.e., 12 days). Furthermore, the possible impacts of environmental factors acquired from the Wudaoliang meteorological station, including air temperature, ground surface temperature, snow depth, and precipitation, on scattering and coherence are discussed. This study explores that Sentinel-1 has great potential for soil F/T monitoring in the QTP, which can indicate F/T states of the surface soil as well as F/T information of the deeper soil with a high spatial–temporal resolution.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Oil Spill SAR Image Segmentation via Probability Distribution Modeling

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      Authors: Fang Chen;Aihua Zhang;Heiko Balzter;Peng Ren;Huiyu Zhou;
      Pages: 533 - 554
      Abstract: Segmentation of marine oil spills in synthetic aperture radar (SAR) images is a challenging task because of the complexity and irregularities in SAR images. In this work, we aim to develop an effective segmentation method which addresses marine oil spill identification in SAR images by investigating the distribution representation of SAR images. To seek effective oil spill segmentation, we revisit the SAR imaging mechanism in order to attain the probability distribution representation of oil spill SAR images, in which the characteristics of SAR images are properly modelled. We then exploit the distribution representation to formulate the segmentation energy functional, by which oil spill characteristics are incorporated to guide oil spill segmentation. Moreover, the oil spill segmentation model contains the oil spill contour regularization term and the updated level set regularization term which enhance the representational power of the segmentation energy functional. Benefiting from the synchronization of SAR image representation and oil spill segmentation, our proposed method establishes an effective oil spill segmentation framework. Experimental evaluations demonstrate the effectiveness of our proposed segmentation framework for different types of marine oil spill SAR image segmentation.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimating Tree Structural Parameters via Automatic Tree Segmentation From
           LiDAR Point Cloud Data

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      Authors: Kenta Itakura;Satoshi Miyatani;Fumiki Hosoi;
      Pages: 555 - 564
      Abstract: In this article, we proposed an automated tree segmentation method using light detection and ranging (LiDAR) point cloud data. Tree segmentation was performed accurately even with bumpy ground, and was validated on more than 1000 samples. For example, 371 out of 374 trees were detected from dataset 2, and the error was caused by the trees with low point densities located in the area far from the LiDAR. Segmentation was accurately performed, including the branches, leading to the retrieval of high-level parameters such as the leaf areas. To obtain the parameters regarding the leaf area from the segmented trees, a method for classifying the leaf and branch points in the three-dimensional point clouds obtained using a terrestrial LiDAR method was proposed. After preprocessing the input point cloud, such as by voxelization, the fast point feature histogram (FPFH) features were calculated. Then, the classifier for classification into leaves and branches was trained using the training dataset to calculate the test accuracy with the test data. Moreover, an unsupervised method for classification using the FPFH feature and k-means algorithm was also performed. Consequently, the recall and precision values of the classification were determined as 98.14% and 96.03%, respectively, with the supervised approach.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • On Circuit-Based Hybrid Quantum Neural Networks for Remote Sensing Imagery
           Classification

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      Authors: Alessandro Sebastianelli;Daniela Alessandra Zaidenberg;Dario Spiller;Bertrand Le Saux;Silvia Liberata Ullo;
      Pages: 565 - 580
      Abstract: This article aims to investigate how circuit-based hybrid quantum convolutional neural networks (QCNNs) can be successfully employed as image classifiers in the context of remote sensing. The hybrid QCNNs enrich the classical architecture of convolutional neural networks by introducing a quantum layer within a standard neural network. The novel QCNN proposed in this work is applied to the land-use and land-cover classification, chosen as an Earth observation (EO) use case, and tested on the EuroSAT dataset used as the reference benchmark. The results of the multiclass classification prove the effectiveness of the presented approach by demonstrating that the QCNN performances are higher than the classical counterparts. Moreover, investigation of various quantum circuits shows that the ones exploiting quantum entanglement achieve the best classification scores. This study underlines the potentialities of applying quantum computing to an EO case study and provides the theoretical and experimental background for future investigations.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Low-Rank Tensor Optimization With Nonlocal Plug-and-Play Regularizers for
           Snapshot Compressive Imaging

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      Authors: Huan Li;Xi-Le Zhao;Jie Lin;Yong Chen;
      Pages: 581 - 593
      Abstract: The increasing volume of hyperspectral images (HSIs) brings great challenges to storage and transmission. Recently, snapshot compressive imaging (SCI), which compresses 3-D HSIs into 2-D measurements, has received increasing attention. Since the original HSIs can be naturally represented as third-order tensors, in this work, we reformulate the degradation model in the SCI systems as a tensor-based form, which friendly allows us to explore the underlying low-rank tensor structure of HSIs. To address the ill-posed SCI reconstruction problem, we suggest a global low-rank tensor optimization model with nonlocal plug-and-play (PnP) regularizers (GNLR) to reconstruct the HSI from the 2-D measurement, which flexibly and collaboratively integrates three-directional tensor nuclear norm (3DTNN) and two implicit nonlocal regularizers. More concretely, 3DTNN characterizes the global correlation of the underlying HSIs. Two implicit regularizers under the PnP framework exploit the benefits of the transformed sparse and low-rank priors on similar patches of the coefficient tensor, respectively. Based on the alternating direction method of multipliers algorithm, we develop an efficient algorithm to tackle the proposed model. Extensive experiments on remotely sensed HSIs and real-world HSIs demonstrate the superiority of the proposed GNLR method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Mission Replanning for Multiple Agile Earth Observation Satellites Based
           on Cloud Coverage Forecasting

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      Authors: Yi Gu;Chao Han;Yuhan Chen;Wei W. Xing;
      Pages: 594 - 608
      Abstract: Recent decades have witnessed a tremendous growth in the number of Earth observation satellites (EOSs), which presents a huge challenge for mission planning. For the EOSs with optical sensors particularly, the observation mission is significantly influenced by the uncertainty of cloud coverage, which has been identified as the most dominant factor for the invalidation of remote sensing images. To overcome this uncertainty, uncertainty programming methods, namely, chance constraint programming (CCP), stochastic expectation model, and robust optimization, are put forth. Despite their success, these approaches are limited in that they simplified the complex cloud coverage uncertainty, which may be different from the true cloud conditions, and they did not take the true cloud information into consideration. Motivated by these recent trends toward Big Data of satellite cloud images and machine learning for spatiotemporal prediction, this article explores a dynamic replanning scheme for multiple EOSs based on cloud forecasting. Specifically, we propose a new approach mainly in the following three steps: first, proactive scheduling based on a CCP is implemented and uploaded via ground control; second, cloud forecasting can be continuously conducted relying on the predictive recurrent neural network and the latest satellite cloud image; and third, mission replanning can be conducted according to the initial schedule and relatively accurate cloud information. Simulation results show that the cloud forecasting method is effective, and the replanning approach presents highly efficient and accurate scheduling results.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SGML: A Symmetric Graph Metric Learning Framework for Efficient
           Hyperspectral Image Classification

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      Authors: Yunsong Li;Bobo Xi;Jiaojiao Li;Rui Song;Yuchao Xiao;Jocelyn Chanussot;
      Pages: 609 - 622
      Abstract: Recently, the semi-supervised graph convolutional network (SSGCN) has been verified effective for hyperspectral image (HSI) classification. However, constrained by the limited training data and spectral uncertainty, the classification performance is remained to be further improved. Moreover, attribute to the massive data, the SSGCN with complex computation is generally too time- and resource-consuming to be applicable in real-time needs. To conquer these issues, we propose an efficient symmetric graph metric learning (SGML) framework by incorporating metric learning into the SSGCN paradigm. Specifically, we first conduct multilevel pixel-to-superpixel projection (P-SP) on the HSI to investigate the multiscale spatial information, where the suitable superpixel numbers are adaptively determined. Then, to extract more expressive representations, we design a new structure denoted as GSvolution, comprising the graph convolution (G-Conv) and a novel self-channel-enhanced convolution (S-Conv), to propagate the labeled and unlabeled graph node information and simultaneously enhance the critical intranode channel features. Finally, the superpixel node features are reprojected to the pixel level (SP-P) so that the distilled multistream features can be integrated to obtain the final decision. Noticeably, this ingenious symmetric mechanism (P-SP and SP-P) can alleviate the spectral variability and facilitate the framework to be an efficient model. Furthermore, in the metric learning module, we propose an innovative metric loss function to enhance the discrimination of the embedding features, i.e., inter class far apart and intraclass close. In the experiments, we demonstrate that the classification capacity of the proposed SGML can surpass the comparators on three benchmark data sets.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Geostationary Microwave Sounder: Design, Implementation and Performance

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      Authors: Bjorn Lambrigtsen;Pekka Kangaslahti;Oliver Montes;Noppasin Niamsuwan;Derek Posselt;Jacola Roman;Mathias Schreier;Alan Tanner;Longtao Wu;Igor Yanovsky;
      Pages: 623 - 640
      Abstract: A geostationary microwave sounder, capable of providing continuous monitoring of temperature, water vapor, clouds, precipitation, and wind in the presence of clouds and precipitation is now feasible. A design called the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR) has been developed at the Jet Propulsion Laboratory, and the required new technology has been developed and is sufficiently mature that a space mission can be initiated. GeoSTAR can be thought of as “AMSU in GEO,” i.e., it has capabilities in geostationary earth orbit (GEO) similar to those of microwave sounders currently operating in low earth orbit. Having such a capability in GEO will add tremendously to our ability to observe dynamic atmospheric phenomena, such as hurricanes and severe storms, monsoonal moisture flow, and atmospheric rivers. GeoSTAR will make measurements every 15 min or less instead of every 12 h and cover a large portion of the Earth continuously instead of with snapshots in a narrow swath. By tracking water vapor patterns, it is also possible to derive atmospheric wind speed and direction at altitudes from the surface to 10–15 km. All of this can be done regardless of cloud cover and weather conditions. During the latter half of 2020, a detailed study of GeoSTAR and its projected performance was undertaken as one of several such studies commissioned by the National Oceanic and Atmospheric Administration (NOAA) for the purpose of configuring NOAA's next generation of earth environmental satellite systems. We present a summary of our findings, including instrument characteristics, measurement accuracy and precision, and expected impact on weather prediction and applications.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Imagery Classification via Random Multigraphs Ensemble
           Learning

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      Authors: Yanling Miao;Mulin Chen;Yuan Yuan;Jocelyn Chanussot;Qi Wang;
      Pages: 641 - 653
      Abstract: Hyperspectral imagery (HSI) classification, which attempts to assign hyperspectral pixels with proper labels, has drawn significant attention in various applications. Recently, the graph-based semisupervised learning (SSL) approaches have shown the outstanding ability to handle the situation of limited labeled data for classification task. However, it is hard to construct the pairwise adjacent graph due to the high dimensionality of hyperspectral data. Besides, the graph-based SSL models are usually decided by a single classifier, which fail to effectively learn the complex structures and intrinsic properties of HSI. To address these problems, we propose a novel graph-based SSL classification model for HSI, which is based on random multigraphs construction and ensemble strategy (RMGE). Specifically, the anchor graph (AG) is constructed with spatial–spectral features, which integrates the spatial characteristics extracted by local binary pattern on each selected spectrum, preserving fine structures of local region. In order to enhance the discriminative capability of the classifier and avoid the trivial solution, the maximum entropy regularization is added into adjacent AG model. In addition, to capture the diversity of HSI data effectively, we design the ensemble framework by employing multiple AGs to learn HSI features. Experiments conducted on real hyperspectral datasets indicate that the proposed RMGE shows better performance than that of state-of-the-art approaches.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Evaluation of Sea Surface Temperatures Derived From the HY-1D Satellite

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      Authors: Xiaomin Ye;Jianqiang Liu;Mingsen Lin;Jing Ding;Bin Zou;Qingjun Song;Yue Teng;
      Pages: 654 - 665
      Abstract: Global sea surface temperatures (SSTs) are detected by the Chinese ocean color and temperature scanner (COCTS) instruments aboard the HaiYang (HY)-1C and HY-1D satellites. In this study, the SSTs derived from the COCTS instrument on the HY-1D (COCTS/HY-1D) satellite and a nonlinear SST algorithm with corresponding coefficients were introduced. The COCTS/HY-1D SSTs recorded from April 26 to August 31, 2021, were evaluated against water temperature measurements taken at depths above 1 m from the in situ Quality Monitor system; root-mean-square errors (RMSEs) of 0.65 and 0.71 °C and robust standard deviations (RSDs) of 0.51 and 0.47 °C were obtained for the daytime and nighttime SSTs, respectively, using a spatiotemporal matching window of 4 h and 2.5 km. Daily gridded SSTs derived from COCTS/HY-1D were compared with those obtained from the visible infrared imaging radiometer suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite in the same period, and RMSEs of 0.67 ± 0.06 and 0.81 ± 0.06 °C and RSDs of 0.49 ± 0.04 and 0.58 ± 0.05 °C were obtained for the daytime and nighttime SSTs, respectively. The COCTS/HY-1D-derived SSTs covering Gulf Stream waters were cross-validated against the VIIRS/S-NPP data as a case study, and RMSEs of 0.53 and 0.47 °C for the daytime and nighttime, respectively, were obtained.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Ships Detection in SAR Images Based on Anchor-Free Model With Mask
           Guidance Features

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      Authors: Haicheng Qu;Lei Shen;Wei Guo;Junkai Wang;
      Pages: 666 - 675
      Abstract: Ship targets in synthetic aperture radar (SAR) images have various scales. The detection model based on anchor boxes requires manual design of candidate boxes, which are fixed and cannot completely match all kinds of targets. Instead, large of anchor boxes with different sizes also result in large amounts of computing resources being consumed. Another potential issue comes from complex background information of near-coast scenes, which leads to ship targets being unrecognized because the background contains similar appearing objects. Therefore, this article proposes an anchor-free detection model based on mask guidance features, which achieves detection mainly through three modifications. First, feature maps of multiple scales are fused to obtain high-resolution feature maps containing rich semantic information. Second, a transformer encoder module is introduced to focus on the context relationship between the target object and the global image and to enhance the dependence between ship targets. Third, the mask guide feature is used to highlight the positions of the target in the feature map, and a loss function in the mask guide mechanism is designed to optimize the mask feature map to reduce false detections and missed detections. Testing the model on the public dataset SAR ship detection dataset, the model's detection accuracy reached 96.17%, with its accuracy on small-size ships reaching 96.11% and 97.84% on large ships.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hypergraph Convolutional Subspace Clustering With Multihop Aggregation for
           Hyperspectral Image

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      Authors: Zijia Zhang;Yaoming Cai;Wenyin Gong;Pedram Ghamisi;Xiaobo Liu;Richard Gloaguen;
      Pages: 676 - 686
      Abstract: Subspace clustering methods have become a powerful tool to cluster hyperspectral imaging (HSI) data as they ensure theoretical guarantees and empirical success. However, existing methods explore subspace representation in the Euclidean space, and thus, failing to exploit the high-order relationship and long-range interdependences. This article presents a simple yet effective method, to extend subspace clustering into the non-Euclidean domain entitled hypergraph convolutional subspace clustering (HGCSC). Instead of treating HSI as Euclidean data only, we represent all the intraclass relations as hyperedges in a hypergraph. With this representation, we can recast the classic self-expression as a hypergraph convolutional self-representation model. To explore the long-range neighboring relation, we introduce a multihop hypergraph convolution process into the method by collapsing the repeated multiplications into a single matrix. HGCSC adopts the Frobenius norm to ensure a closed-form solution. We assess the performance of HGCSC on five real HSI datasets and show that HGCSC significantly outperforms competitors in terms of clustering accuracy.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Practical Temperature and Emissivity Separation Framework With
           Reanalysis Atmospheric Profiles for Hyper-Cam Airborne Thermal Infrared
           Hyperspectral Imagery

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      Authors: Lyuzhou Gao;Yanfei Zhong;Liqin Cao;Jiani He;Xuhe Zhu;
      Pages: 687 - 699
      Abstract: Compared with land surface temperature (LST) and land surface emissivity (LSE) retrieval from single-band or multispectral thermal infrared (TIR) data, TIR hyperspectral imagery allows us to obtain accurate LST and LSE through the use of an automatic temperature and emissivity separation (TES) method. However, the existing TES algorithms have rarely been investigated with airborne TIR hyperspectral imagery from the Hyper-Cam sensor, which is based on Fourier transform infrared technology. In this article, a practical LST and LSE retrieval framework incorporating reanalysis atmospheric profiles is proposed for use with Hyper-Cam airborne TIR hyperspectral imagery. In this framework, an atmospheric compensation method is introduced based on spatiotemporal analysis and the fusion of three types of widely used atmospheric profiles to replace the unattainable synchronously measured atmospheric profiles. An empirically constrained TES method is then proposed to extend the original TES algorithm to Hyper-Cam hyperspectral imagery. In addition, to exclude the negative effects of radiometric calibration error, measurement noise, and the atmospheric absorption lines in certain bands, the problematic bands are removed to improve the data quality. To evaluate the performance of the proposed framework, a set of airborne TIR hyperspectral imagery with 81 bands was acquired using the Hyper-Cam airborne system. Experiments were carried to compare the performance of the proposed method and the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes-Infrared (FLAASH-IR) method. The results indicate that the proposed method can obtain more robust and accurate results than FLAASH-IR, and the root-mean-square error (RMSE) of the emissivity data is around 0.015 for all the validation samples.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Discriminative Context-Aware Network for Target Extraction in Remote
           Sensing Imagery

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      Authors: Lei Hu;Chuang Niu;Shenghan Ren;Minghao Dong;Changli Zheng;Wei Zhang;Jimin Liang;
      Pages: 700 - 715
      Abstract: Extracting objects of interest from remote sensing imagery is an essential part in various practical applications. The objects that people pay attention to in the remote sensing scene mainly include buildings, roads, vehicles, etc. In this article, extracting the aforementioned objects are collectively referred to as the target extraction task. Arising from object scale variation, appearance similarity between adjacent patches, diversity of imaging orientation, and complexity of background, it is difficult to extract complete objects from cluttered backgrounds. Deep neural network has made great achievement in dense prediction for target extraction. However, most of the previous works are still faced with a formidable challenge in discriminative context feature representation to extract targets of various categories and correctly classify pixels around the boundary. In this article, we propose a target extraction neural network, named discriminative context-aware network, to focus on discriminative high-level context features and preserve spatial information. First, a discriminative context-aware feature module is designed to generate the feature maps in the top layer, which not only captures the rich image context information but also aggregates the contrasted local information at multiple scales. Second, a refine decoder module is adopted to preserve spatial information from low-level layers and enhance the feature representation, leading to precise segmentation results. We conducted extensive experiments on building and road extraction benchmarks, including WHU building dataset and Massachusetts road dataset, together with a self-constructed dataset for vehicle extraction in SAR images. Our method achieves state-of-the-art results with fewer parameters and faster inference.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Image Denoising via Low-Rank Representation and CNN Denoiser

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      Authors: Hezhi Sun;Ming Liu;Ke Zheng;Dong Yang;Jindong Li;Lianru Gao;
      Pages: 716 - 728
      Abstract: Hyperspectral images (HSIs) are widely used in various tasks such as earth observation and target detection. However, during the imaging process, HSIs are often corrupted by various noises. In this article, we firstly investigate the advantages of traditional physical restoration models and the denoising convolutional neural networks (CNN) for HSIs denoising tasks. The sparse based low-rank representation can explore the global correlations in both the spatial and spectral domains, and the CNN-based denoiser can represent the deep prior which cannot be designed by traditional restoration models. Then, we propose a HSI denoising model with low-rank representation and CNN denoiser prior in the flexible and extensible plug-and-play framework by combining the advantages of the two methods. The proposed model is user-friendly, requiring no retraining. Simulated data experiments show that, compared with competitive methods, the proposed one achieves better denoising results for both additive Gaussian noise and Poissonian noise in various quantitative evaluation indicators. Real data experiments show that the proposed model yields the best performance.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Feasibility Study of Wood-Leaf Separation Based on Hyperspectral LiDAR
           Technology in Indoor Circumstances

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      Authors: Hui Shao;Zheng Cao;Wei Li;Yuwei Chen;Changhui Jiang;Juha Hyyppä;Jie Chen;Long Sun;
      Pages: 729 - 738
      Abstract: Wood–leaf separation aiming at classifying LiDAR points into wood and leaf components is one of the most important genres for improving leaf area index estimation and forestry survey accuracy. The wood return signals could artificially increase the apparent foliage content, which needs to be screened out for deriving vital tree attributes accurately. Previous research works tended to use intensity, waveform, and geometric information extracted from a single wavelength LiDAR for wood–leaf separation. This article employs a revised hyperspectral LiDAR (HSL) to obtain spatial and ultrawide spectral data simultaneously. We also propose a simple three steps method to separate wood and leaf components based on HSL spatial and spectral measurements under the laboratory circumstances. First, the preprocessing is conducted to acquire 3-D spatial information and the multiband laser pulse reflectance for further separation. Second, preliminary separation (band division, key feature parameter calculation, and judgment) is implemented based on reflectance. Third, we employ K-Nearest Neighbor (KNN) method to enhance separation results based on preliminary separation results and spatial features and then update the results by recorrection. Then, 3-D reconstruction is accomplished by fusing wood–leaf separation results. The experimental results demonstrate that the proposed method can separate wood and leaf components with high accuracy and indicate tree attributes straightforwardly.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Monitoring Large-Scale Hydraulic Engineering Using Sentinel-1 InSAR: A
           Case Study of China's South-to-North Water Diversion Middle Route
           Project

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      Authors: Nan Wang;Jie Dong;Zhigang Wang;Jinfeng Lei;Lu Zhang;Mingsheng Liao;
      Pages: 739 - 750
      Abstract: The Middle Route of South-to-North Water Diversion (SNWD) Project,known as one of the greatest hydraulic projects of China, undertakes most of domestic and industrial water use in the Beijing–Tianjin area, as well as a part of ecological water use. The whole canal should be monitored to ensure a continuous water supply for water-receiving areas. Compared with traditional ground-based monitoring tools limited by the low distribution density, intensive labor force, and huge cost, the satellite synthetic aperture radar interferometry (InSAR) with wide coverage, high-frequency revisiting, and low cost is more suitable for monitoring large-scale infrastructures. In this study, we employed the Sentinel-1 data to monitor the canal of the Middle Route of the SNWD Project within Henan Province. The deformation rates along the canal were obtained by using persistent scatterer InSAR, from which a total of 20 deformed canal sections and seven suspected deformed canal sections were identified. Among them, the Shahe aqueduct is overall stable except for the Shahe Beam Aqueduct and Lushanpo Landing Aqueduct. The Yuzhou-Changge canal passes through a subsidence funnel with a maximum deformation rate of about –20 mm/year. The InSAR-derived deformation coincides well with the in-situ leveling measurements over the Yuzhou-Changge canal. Their correlation is 0.95 and the root mean square error of their differences is 2.41 mm/year. The results demonstrate the effectiveness of satellite InSAR for monitoring large-scale hydraulic engineering. It can be combined with ground leveling to achieve overall investigations and detailed monitoring, largely improving the efficiency and cost of the current ground monitoring system.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Assessment and Validation of Snow Liquid Water Retrievals in the Antarctic
           Ice Sheet Using Categorical Triple Collocation

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      Authors: Yong Liu;Chunxia Zhou;Lei Zheng;
      Pages: 751 - 763
      Abstract: Snow liquid water produced by melting can affect the surface mass and energy balance in the Antarctic Ice Sheet (AIS). It is essential for monitoring the occurrence of snow liquid water (OLW) within the snowpack. Spaceborne microwave sensors (i.e., scatterometer and radiometer) and climate models are primary tools for examining the OLW in the AIS. However, implementing the complementary nature of measurements and comparing their observations quantitatively are challenging owing to sparse snow wetness validation data. In this article, we use categorical triple collocation to rank the relative performance of OLW products. According to the first rankings, we construct an ensemble OLW product that is more consistent with the results detected by in situ station air temperature data than single data source products. Furthermore, we quantitatively estimate the proportion correct of wet snow (sensitivity) and the proportion correct of dry snow (specificity) of the measurements on the Antarctic Peninsula (AP). The scatterometer demonstrates high balanced accuracy (a binary-variable performance metric) on the AP (up to 0.899); however, this result does not signify that the scatterometer is the optimal observation in any period. The radiometer and climate model show high sensitivities in different melt stages but underestimate wet snow extent during certain periods. The quantitative estimation provides a new perspective for comparing various observations and detection methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Microwave Sensors—Advantages and Limitations

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      Authors: Christian D. Kummerow;Joseph C. Poczatek;Scott Almond;Wesley Berg;Olivia Jarrett;Andrew Jones;Michael Kantner;Chia-Pang Kuo;
      Pages: 764 - 775
      Abstract: Temperature and humidity soundings form the bedrock of modern data assimilation due to their ability to directly constrain the atmospheric state variables. Because of their ability to penetrate clouds and work in all weather conditions, microwave sounders have very large impacts on constraining numerical weather prediction models. Recent advancements in integrated microwave assembly, space-grade high speed analog to digital converters, gigabit-per-second data interconnects, and field programmable gate arrays have enabled a transition from traditional analog detector-based demodulators to digitally channelized systems that allow for hyperspectral, or fine spectral resolution microwave sounders to be viable replacements to the current operational instruments. This article demonstrates that retrievals of temperature and moisture soundings can be improved by as much as 50% when 60–80 appropriately chosen pseudochannels are employed. While the current simulations were limited to cloud free oceans, perhaps even greater benefits can be realized over land and cloud conditions where additional channels can help constrain the surface and clouds. The article also demonstrated the advantages of hyperspectral sensors as a way to detect radio frequency interference in the few Kelvin range, as well as its ability to improve intercalibration efforts due to its ability to match frequency response functions of target sensors.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Editorial Foreword to the Special Issue on Recent Advances in
           Multitemporal Remote-Sensing Data Processing

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      Authors: Sicong Liu;Francesca Bovolo;Lorenzo Bruzzone;Xiaohua Tong;Qian Du;
      Pages: 776 - 778
      Abstract: This special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topics in the multitemporal data analysis.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Leveraging NASA Soil Moisture Active Passive for Assessing Fire
           Susceptibility and Potential Impacts Over Australia and California

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      Authors: Nazmus Sazib;John D. Bolten;Iliana E. Mladenova;
      Pages: 779 - 787
      Abstract: Wildfires are a major concern around the globe because of the immediate impact they have on people's lives, local ecosystems, and the environment. Soil moisture is one of the most important factors that influence wildfire occurrences and spread. However, it is also one of the most challenging hydrological variables to measure routinely and accurately. Therefore, soil moisture is significantly underutilized in operational wildfire risk applications. Thus, the aim here is to use a well-established operational soil moisture product to isolate the soil moisture-fire relationship and assess the utility of using soil moisture as a leading indicator of potential fire risk. We evaluated the value of remotely-sensed soil moisture observations from the soil moisture active passive sensor for monitoring and predicting fire risk in Australia and California. We quantified the relationship between observed fire activity and soil moisture conditions and analyzed the soil moisture conditions for two extreme fire events. Our findings show that fire activity is strongly associated with soil moisture anomalies. Lagged correlation analysis demonstrated that a remote-sensing based soil moisture product could predict fire activity with a 1–2 month lead-time. Soil moisture anomalies consistently decreased in the months preceding fire occurrence, often from normal to drier conditions, according to a spatiotemporal analysis of soil moisture in two extreme fire events. Overall, our findings indicate that soil moisture conditions prior to large wildfires can aid in their prediction and operational satellite-based soil moisture products such as the one used here have real value for supporting wildfire susceptibility and impacts.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Signal Separation in GEO SAR Imaging of Maneuvering Ships by Removing
           Micro-Motion Effect

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      Authors: Jindong Yu;Ze Yu;Yukun Guo;Chunsheng Li;
      Pages: 788 - 803
      Abstract: Geosynchronous synthetic aperture radar system provides the capability of continuous observation for several hours, which is of great significance for ship monitoring. The complex motion of maneuvering ship during long integration time makes the signals of different targets interfere with each other, resulting in the micro-motion effect, which degrades the quality of the image. The micro-motion of the expected target is first compensated for by the generalized radon-Fourier transform based method. The key of the algorithm is to enlarge the range cell migration of the interfering target while maintaining the energy of the expected target in a range bin based on the Keystone transform (KT) and inverse KT. Combined with the L-statistics method, the proposed algorithm can separate signals effectively. Validation results based on simulation and airborne experiments show that the proposed algorithm achieves good signal separation performance.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Superpixel Generation for SAR Imagery Based on Fast DBSCAN Clustering With
           Edge Penalty

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      Authors: Liang Zhang;Shengtao Lu;Canbin Hu;Deliang Xiang;Tao Liu;Yi Su;
      Pages: 804 - 819
      Abstract: In this article, we propose an adaptive superpixel generation algorithm for synthetic aperture radar (SAR) imagery, which is implemented based on fast density-based spatial clustering of applications with noise (DBSCAN) clustering and superpixel merging with edge penalty. The superpixel generation algorithm consists of two stages, i.e., fast pixel clustering and superpixel merging. In the clustering stage, we define a new adaptive pixel dissimilarity measure for SAR image and then optimize the DBSCAN strategy, which considers the edge information and can achieve rapid clustering. In the merging stage, based on the initial superpixels, a new superpixel dissimilarity measure is defined, which can merge the small local superpixels into their neighborhood superpixels, making the final superpixel segmentation results compact and regular. Experimental results on two simulated and two real SAR images demonstrate that our method outperforms the state-of-the-art superpixel generation methods in terms of both efficiency and accuracy. The superpixel segmentation accuracy of our method is 5–10% higher and the time cost is 10–40% lower than other methods. Since the superpixel segmentation result can be used as a preprocessing stage for the SAR data interpretation applications, superpixel-based and pixel-based classification results with two real SAR images are also used for comparison, which can validate the advantages of our proposed method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Microwave Radiometer MTVZA-GY on New Russian Satellite Meteor-M No. 2-2
           and Sudden Stratospheric Warming Over Antarctica

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      Authors: Leonid M. Mitnik;Vladimir P. Kuleshov;Maia L. Mitnik;Grigory M. Chernyavsky;Igor V. Cherny;Andrey M. Streltsov;
      Pages: 820 - 830
      Abstract: The Meteor-M No. 2-2 meteorological satellite with the microwave radiometer MTVZA-GY on board was launched into a circular sun-synchronous orbit on July 5, 2019. The radiometer conducts a conical scanning at 65-degree incidence angle and receives the Earth's outgoing radiation in the frequency range ν ≈ 6–190 GHz. The swath width is 2500 km on ascending orbits and 1500 km on descending orbits. The parameters of the ocean, land surface, and troposphere are extracted from brightness temperatures TB(ν) measured at imager frequencies of 6.9, 10.65, 18.7, 23.8, 31.5, 36.5, 42, 48, and 91.65 GHz in vertical and horizontal polarizations. Measurements at the sounder frequencies (ten channels in the 52–58 GHz oxygen absorption band and three channels in the strong water vapor resonance line region centered at 183.31 GHz) provide information on air temperature and humidity in the troposphere and stratosphere. The structure and development of dynamic atmospheric phenomena of synoptic scale are imprinted on the global TB(ν) maps at imager frequencies. The TB(ν) time series at the sounder frequencies allowed us to detect and trace the evolution of a rare phenomenon — a sudden stratospheric warming over Antarctica in August–September 2019.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • $L$ -Band+Scaling+Reference&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&rft.date=2022&rft.volume=15&rft.spage=831&rft.epage=848&rft.aulast=Dorigo;&rft.aufirst=Rémi&rft.au=Rémi+Madelon;Nemesio+J.+Rodríguez-Fernández;Robin+van+der+Schalie;Tracy+Scanlon;Ahmad+Al+Bitar;Yann+H.+Kerr;Richard+de+Jeu;Wouter+Dorigo;">Toward the Removal of Model Dependency in Soil Moisture Climate Data
           Records by Using an $L$ -Band Scaling Reference

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      Authors: Rémi Madelon;Nemesio J. Rodríguez-Fernández;Robin van der Schalie;Tracy Scanlon;Ahmad Al Bitar;Yann H. Kerr;Richard de Jeu;Wouter Dorigo;
      Pages: 831 - 848
      Abstract: Building climate data records of soil moisture (SM) requires computing long time series by merging retrievals from sensors on-board different satellites, which implies to perform a bias correction or rescaling on the original time series. Due to their long time span and high temporal frequency, model data could be used as a common reference for the rescaling. However, avoiding model dependence in observational climate data records is needed for some applications. In this article, the possibility of using as reference remote sensing data from one of the $L$-band sensors specifically designed to measure SM is discussed. Advanced Microwave Scanning Radiometer 2 SM time series were rescaled by matching their cumulative distribution functions (CDFs) to those of Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Global Land Data Assimilation System (GLDAS) NOAH model time series. The CDF computation was investigated as a function of the time series length, finding significant differences from four to nine years. Replacing temporal by spatial variance does not allow us to compute better CDFs from short time series. The rescaled time series show a high correlation ($R>0.8$) to the original ones and a low bias with respect to the reference ($< $0.03 m $^{3}cdot$ m$^{-3}$). The time series rescaled using several SMOS or SMAP datasets were also evaluated against in situ measurements and show performances similar to or slightly better than those rescaled using the model GLDAS. The impact of random errors and gaps of the observational data into the res-aling was evaluated. These results show that it is actually possible to use $L$-band data as reference to rescale time series from other sensors to build long time series of SM.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Land Surface Albedo Estimation With Chinese GF-1 WFV Data in Northwest
           China

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      Authors: Hongmin Zhou;Zhe Wang;Wu Ma;Tao He;Huawei Wan;Jindi Wang;Shunlin Liang;
      Pages: 849 - 861
      Abstract: Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimation of Snow Mass Information via Assimilation of C-Band Synthetic
           Aperture Radar Backscatter Observations Into an Advanced Land Surface
           Model

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      Authors: Jongmin Park;Barton A. Forman;Sujay V. Kumar;
      Pages: 862 - 875
      Abstract: This study assimilated Sentinel-1 C-band backscatter observations over snow-covered terrain into the Noah-Multiparameterization land surface model using support vector machine (SVM) regression and an ensemble Kalman filter to improve the modeled terrestrial snow mass estimates. The data assimilation (DA) experiment was conducted across Western Colorado from September 2016 to August 2017. As part of the DA experiments, the impact of a rule-based update was evaluated by comparing snow water equivalent (SWE) estimates via DA (with [${rm {DA}}_{rm {v1}}$] and without [${rm {DA}}_{rm {v2}}$] the rule-based update) against SNOTEL SWE measurements. Results confirmed that rule-based update helped minimize SVM controllability issues, and in turn, improved the accuracy of SWE estimates relative to both open loop (OL) and ${rm {DA}}_{rm {v2}}$. Comparison of SWE estimates from Sentinel-1 ${rm {DA}}_{rm {v1}}$ against SNOTEL SWE revealed that 75% of stations showed improvements in bias and correlation coefficient relative to the OL. Assimilated SWE estimates also showed statistical improvements during both the snow accumulation and snow ablation periods. However, unbiased root mean square error showed a slight increase during the snow ablation period due to the large variability in the electromagnetic response of C-band backscatter over deep and/or wet snow. Improvement of the SWE estimates also resulted in improving river discharge estimates compared to in situ measurements. River discharge using Sentinel-1 ${rm {DA}}_{rm {v1}}$ improved the Nash–Sutcliffe efficie-cy at all available stations. These results suggest that physically constrained SVM can serve as an efficient observation operator for snow mass DA through explicit consideration of the first-order C-band scattering mechanisms over different terrestrial snow conditions.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Incremental Knowledge Extraction From IoT-Based System for Anomaly
           Detection in Vegetation Crops

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      Authors: Danilo Cavaliere;Sabrina Senatore;
      Pages: 876 - 888
      Abstract: Precision agriculture systems collect spectral images from satellites, from which vegetation indices (VIs) can be assessed to monitor vegetation and soil condition. It requires a near-daily data acquisition to perform robust crop monitoring and data analysis. Satellites provide a periodic data acquisition that need a further data integration using multiple satellite sources along with camera-equipped drones to achieve an accurate data collection on a selected area. Moreover, VIs are not enough for a proper vegetation evaluation of the monitored areas due to differences among cultivars, the phenological season in which the vegetation is evaluated, the latitude of the areas, etc. This article introduces a system model to detect anomalies regarding the vegetation and soil conditions according to the area phenology and the historical vegetation trends. The system collects spectral images of the regions of interest (ROIs) from satellites and drones, harmonized to calculate VIs and feeds a dataset of near-daily high-resolution integrated images. The harmonic analysis allows phenological data extraction about the ROIs, hence the territorial observation model (TOM) has been extended to represent phenological stages and build knowledge on the ROIs and their phenology that is stored on a triple store. The system selects the VI values, calculated during the learned growing seasons of the ROIs, and classifies them to detect vegetation anomalies affecting those ROIs. The collected knowledge can be used by end-users (e.g., agronomists, experts, etc.) to analyze the anomalies correlated to historical results and vegetation trends.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Parametric-Model-Based Approach for Atmospheric Phase Screen Removal in
           Ground-Based Interferometric SAR

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      Authors: Elisa Giusti;Samuele Gelli;Marco Martorella;
      Pages: 889 - 900
      Abstract: The atmosphere affects the propagation of radar signals by provoking unwanted signal phase changes. In interferometric applications, such as coherent change detection and displacement measurements, this effect may significantly degrade the system performances. Moreover, atmosphere-induced phase changes are both time and space variants, and therefore, they are not easy to be removed. This article proposes a novel method to remove atmospheric effects by using a parametric model of the refractive index, which is derived as an extension of the International Telecommunication Union—Radiocommunication model. The proposed algorithm has been tested on real data acquired by using a ground-based synthetic aperture radar system in conjunction with data collected by a weather station. Data have been acquired continuously for three consecutive days, approximatively every 5 min. Results have shown how the proposed method can effectively remove atmospheric effects and restore the signal phase.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Pseudo Quad-Pol Simulation From Compact Polarimetric SAR Data via a
           Complex-Valued Dual-Branch Convolutional Neural Network

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      Authors: Fan Zhang;Zhuoyue Cao;Deliang Xiang;Canbin Hu;Fei Ma;Qiang Yin;Yongsheng Zhou;
      Pages: 901 - 918
      Abstract: Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual-polarization imaging mode. CP synthetic aperture radar has a larger swath and can provide more polarimetric information compared with the traditional dual-polarization imaging mode (HH/HV or VH/VV). Pseudo quad-polarimetric (quad-pol) data reconstruction is an important technology in the application of CP data. The goal of pseudo quad-pol data reconstruction from CP data is to change the form of CP data to the form of quad-pol data without increasing any new information. In this article, a new pseudo quad-pol data reconstruction method from the CP data is proposed. This method combines a complex-valued dual-branch convolutional neural network (CV-DBCNN) to achieve the reconstruction of the pseudo quad-pol data. It utilizes complex-valued convolutional layers and a complex-valued activation function to fully extract the polarimetric information embedded in the complex-valued CP data. For the CV-DBCNN, the branch with 1×1 kernel size is used to nonlinearly and self-adaptively combine the channel of input data, and the branch with 3×3 kernel size is used to extract the discriminative regional polarimetric features. Furthermore, polarimetric decomposition is utilized to evaluate the scattering mechanisms of the pseudo quad-pol data. Three state-of-the-art methods are utilized for comparison. In comparison with other methods, our proposed reconstruction method based on the CV-DBCNN shows its superiority in terms of the pseudo quad-pol data reconstruction and scattering mechanism preservation.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Scattering Mechanism Analysis of Man-Made Targets via Polarimetric SAR
           Observation Simulation

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      Authors: Lamei Zhang;Yifan Chen;Ning Wang;Bin Zou;Shuo Liu;
      Pages: 919 - 929
      Abstract: Scattering mechanism analysis of a man-made target is critical for polarimetric synthetic aperture radar (PolSAR) image feature extraction and image interpretation. The most straightforward method for scattering mechanism investigation of man-made target is to analyze a huge amount of measured data under various conditions. However, the acquirement of measured data is extremely expensive and time consuming, coupled with its poor reusability, which makes the investigation inefficient and costly. The electromagnetic (EM) simulation is an effective and convenient way to obtain PolSAR data by solving equations describing the EM scattering from target of interest. It is adaptable to any observation condition, targets or environments, which have become a powerful tool commonly used for the study of EM scattering mechanism of target. In this article, an efficient simulation framework for the emulation of PolSAR operation and imaging is proposed. This framework is based on ray tracing integrated with geometrical optics, physical optics, and Kirchhoff approximation. The simulated image of a family car is in good agreement with the measured data of MiniSAR platform. Besides, images of T-72 tank under various observation conditions, radar parameters and terrains are simulated. Freeman decomposition is employed and scattering component percentages of target under different conditions are presented to analyze the scattering mechanism in detail.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Lightweight Complex-Valued DeepLabv3+ for Semantic Segmentation
           of PolSAR Image

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      Authors: Lingjuan Yu;Zhaoxin Zeng;Ao Liu;Xiaochun Xie;Haipeng Wang;Feng Xu;Wen Hong;
      Pages: 930 - 943
      Abstract: Semantic image segmentation is one kindof end-to-end segmentation method which can classify the target region pixel by pixel. As a classic semantic segmentation network in optical images, DeepLabv3+ can achieve a good segmentation performance. However, when this network is directly used in the semantic segmentation of polarimetric synthetic aperture radar (PolSAR) image, it is hard to obtain the ideal segmentation results. The reason is that it is easy to yield overfitting due to the small PolSAR dataset. In this article, a lightweight complex-valued DeepLabv3+ (L-CV-DeepLabv3+) is proposed for semantic segmentation of PolSAR data. It has two significant advantages when compared with the original DeepLabv3+. First, the proposed network with the simplified structure and parameters can be suitable for the small PolSAR data, and thus, it can effectively avoid the overfitting. Second, the proposed complex-valued (CV) network can make full use of both amplitude and phase information of PolSAR data, which brings better segmentation performance than the real-valued (RV) network, and the related CV operations are strictly true in the mathematical sense. Experimental results about two Flevoland datasets and one San Francisco dataset show that the proposed network can obtain better overall average, mean intersection over union, and mean pixel accuracy than the original DeepLabv3+ and some other RV semantic segmentation networks.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • VPP: Visibility-Based Path Planning Heuristic for Monitoring Large Regions
           of Complex Terrain Using a UAV Onboard Camera

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      Authors: Andres J. Sanchez-Fernandez;Luis F. Romero;Gerardo Bandera;Siham Tabik;
      Pages: 944 - 955
      Abstract: The use of unmanned aerial vehicles with multiple onboard sensors has grown significantly in tasks involving terrain coverage such as environmental and civil monitoring, disaster management, and forest fire fighting. Many of these tasks require a quick and early response, which makes maximizing the land covered from the flight path a challenging objective, especially when the area to be monitored is irregular, large and includes many blind spots. Accordingly, state-of-the-art total viewshed algorithms can be of great help to analyze large areas and find new paths providing maximum visibility. This article shows how the total viewshed computation is a valuable tool for generating paths that provide maximum visibility during a flight. We introduce a new heuristic called visibility-based path planning (VPP) that offers a different solution to the path planning problem. VPP identifies the hidden areas of the target territory to generate a path that provides the highest visual coverage. Simulation results show that VPP can cover up to 98.7% of the Montes de Malaga Natural Park and 94.5% of the Sierra de las Nieves National Park, both located within the province of Malaga (Spain) and chosen as regions of interest. In addition, a real flight test confirmed the high visibility achieved using VPP. Our methodology and analysis can be easily applied to enhance monitoring in other large outdoor areas.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Constrained-SIoU: A Metric for Horizontal Candidates in Multi-Oriented
           Object Detection

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      Authors: Yanan Zhang;Haichang Li;Rui Wang;Mengya Zhang;Xiaohui Hu;
      Pages: 956 - 967
      Abstract: Intersection over union (IoU) has been widely adopted to evaluate and select candidate regions in multi-oriented object detection. Intuitively, overlaps between candidates and multi-oriented ground-truth boxes make more sense when assessing the quality of horizontal candidates. However, the horizontal minimum bounding box (HMBB) of the ground-truth box is generally used for the IoU calculation in practice, bringing about biased results. In this article, we propose a novel Splicing Intersection over Union (SIoU) to provide a more preferable metric for horizontal candidate selection when detecting multi-oriented objects. By computing the intersection between the candidate region and the ground-truth box rather than its HMBB, SIoU provides a better description of how much object information a candidate contains. Furthermore, we introduce two variants of constraints for the center of each candidate to ensure its location focusing on the objects. Candidates whose centers deviate too far from the objects will be penalized. We integrate the constraint with SIoU, denoted as constrained-SIoU, to select horizontal candidates more efficiently. Comparative experiments on two datasets of aerial images, DOTA and HRSC2016, demonstrate the effectiveness of the proposed method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Image Classification—Traditional to Deep Models: A
           Survey for Future Prospects

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      Authors: Muhammad Ahmad;Sidrah Shabbir;Swalpa Kumar Roy;Danfeng Hong;Xin Wu;Jing Yao;Adil Mehmood Khan;Manuel Mazzara;Salvatore Distefano;Jocelyn Chanussot;
      Pages: 968 - 999
      Abstract: Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics, i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data, make accurate classification challenging for traditional methods. In the last few years, deep learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of TML for HSIC and then we will acquaint the superiority of DL to address these problems. This article breaks down the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial–spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Medium- and Long-Term Precipitation Forecasting Method Based on Data
           Augmentation and Machine Learning Algorithms

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      Authors: Tiantian Tang;Donglai Jiao;Tao Chen;Guan Gui;
      Pages: 1000 - 1011
      Abstract: Accurate medium and long-term precipitation forecasting plays a vital role in disaster prevention and mitigation and rational allocation of water resources. In recent years, there are various methods for medium- and long-term precipitation forecasting based on machine learning algorithms. However, machine learning has a high demand for the size of sample data. Therefore, this article proposes a data augmentation algorithm based on the K-means clustering algorithm and synthetic minority oversampling technique (SMOTE), which can effectively enhance sample information. Besides, through constructing random forest (RF), extreme gradient boosting (XGB), recurrent neural network (RNN), and long short-term memory (LSTM) are, respectively, constructed as the models to forecast monthly grid precipitation of the Danjiangkou River Basin. This study aims to improve the accuracy of medium- and long-term precipitation forecasting. The main results are the following two aspects: 1) in most years, the anomaly correlation coefficient and Pg score of SMOTE-km-XGB and SMOTE-km-RF exceed that of XGB and RF; furthermore, compared with the other three methods, SMOTE-km-XGB method is more suitable for precipitation forecasting in the studied basin in this article; and 2) the forecasting results of two deep learning methods (RNN and LSTM) show that the sample data processed by the K-means clustering algorithm and SMOTE data augmentation algorithm have not achieved considerable results in deep learning. This study improves the accuracy of precipitation forecast by expanding and balancing the information of sample data, and provides a new research idea for improving the accuracy of medium- and long-term hydrological forecasting.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • MSG-SR-Net: A Weakly Supervised Network Integrating Multiscale Generation
           and Superpixel Refinement for Building Extraction From High-Resolution
           Remotely Sensed Imageries

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      Authors: Xin Yan;Li Shen;Jicheng Wang;Xu Deng;Zhilin Li;
      Pages: 1012 - 1023
      Abstract: Weakly supervised semantic segmentation (WSSS) methods based on image-level labels can relieve the tedious pixel-level annotation burden, and these methods are mainly based on class activation maps (CAMs). However, it is challenging to generate high-quality CAMs for high-resolution remotely sensed imagery (HRSI). In this article, we propose a WSSS method for building extraction from HRSI using image-level labels. The proposed method, termed as the MSG-SR-Net, integrates two novel modules, i.e., multiscale generation (MSG) and superpixel refinement (SR), to obtain high-quality CAMs so as to provide reliable pixel-level training samples for subsequent semantic segmentation steps. The MSG module is proposed to use global semantic information to guide the learning of multiple features across different levels, and then, respectively, to utilize multilevel features for generating multiscale CAMs. This component can effectively suppress the interference of the class-irrelevant noise and strengthen the use of profitable information in multilevel features. The SR module is designed to take advantage of superpixels to improve multiscale CAMs in target integrity and details preserving. Extensive experiments on two public building datasets demonstrated that the proposed modules made the MSG-SR-Net obtain more integral and accurate CAMs for building extraction. Moreover, experimental results also showed the proposed method achieved excellent performance with over 67% in F1-score, and outperformed other weakly supervised methods in effectiveness and generalization ability.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Patch-Aware Deep Hyperspectral and Multispectral Image Fusion by Unfolding
           Subspace-Based Optimization Model

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      Authors: Jianjun Liu;Dunbin Shen;Zebin Wu;Liang Xiao;Jun Sun;Hong Yan;
      Pages: 1024 - 1038
      Abstract: Hyperspectral and multispectral image fusion aims to fuse a low-spatial-resolution hyperspectral image (HSI) and a high-spatial-resolution multispectral image to form a high-spatial-resolution HSI. Motivated by the success of model- and deep learning-based approaches, we propose a novel patch-aware deep fusion approach for HSI by unfolding a subspace-based optimization model, where moderate-sized patches are used in both training and test phases. The goal of this approach is to make full use of the information of patch under subspace representation, restrict the scale and enhance the interpretability of the deep network, thereby improving the fusion. First, a subspace-based fusion model was built with two regularization terms to localize pixels and extract texture. Then, the subspace-based fusion model was solved by the alternating direction method of multipliers algorithm, and the model was divided into one fidelity-based problem and two regularization-based problems. Finally, a structured deep fusion network was proposed by unfolding all steps of the algorithm as network layers. Specifically, the fidelity-based problem was solved by a gradient descent algorithm and implemented by a network. The two regularization-based problems were described by proximal operators and learnt by two u-shaped architectures. Moreover, an aggregation fusion technique was proposed to improve the performance by averaging the fused images in all iterations and aggregating the overlapping patches in the test phase. Experimental results, conducted on both synthetic and real datasets, demonstrated the effectiveness of the proposed approach.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multiscale and Direction Target Detecting in Remote Sensing Images via
           Modified YOLO-v4

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      Authors: Zakria Zakria;Jianhua Deng;Rajesh Kumar;Muhammad Saddam Khokhar;Jingye Cai;Jay Kumar;
      Pages: 1039 - 1048
      Abstract: Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Hyperspectral Detection and Unmixing of Subpixel Target Using Iterative
           Constrained Sparse Representation

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      Authors: Qiang Ling;Kun Li;Zhaoxu Li;Zaiping Lin;Jiawen Wang;
      Pages: 1049 - 1063
      Abstract: With great significance in military and civilian applications, subpixel target detection is of great interest in hyperspectral remote sensing. The subpixel targets usually also need to be unmixed to identify their components. Traditionally, these subpixel targets are first detected and then unmixed to obtain their corresponding abundances. Therefore, target detection and target unmixing are independently performed. However, there are potential relations between these two processes that need to be investigated. In this article, we integrate these two processes using iterative constrained sparse representation. The main idea of this algorithm is that each pixel can be linearly and sparsely represented by the prior target spectra and several background endmembers extracted from its neighborhood. Moreover, the sum-to-one and nonnegativity constraints are introduced to ensure the sparse representation coefficients to have physical meaning. Specifically, the background endmembers are automatically extracted from the local background based on an iterative process. Then, the test pixel is represented by these extracted endmembers. Finally, the detection output is determined by the total target abundance and the residuals. The main innovation of this method is that it implements detection and unmixing of subpixel target simultaneously, even if the local background is contaminated by target signals. Experiments conducted on both synthetic and real hyperspectral datasets demonstrate that the proposed detector achieves an outstanding performance on detection and unmixing.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Restoration of Authentic Position of Unidentified Vessels in SAR Imagery:
           A Deep Learning Based Approach

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      Authors: Juyoung Song;Duk-jin Kim;Sangho An;Junwoo Kim;
      Pages: 1064 - 1078
      Abstract: Enhancement of vessel detection performance in synthetic aperture radar (SAR) images generated academic advancements related to amelioration of the algorithmic accuracy and training data procurement. For practical implementation of vessel detection algorithm to maritime surveillance, however, presentation of authentic position of vessels was essential. Accordingly, this article aimed to propose an algorithm, which demonstrated realistic and azimuth shift-corrected position of vessel, especially out of conventional vessel monitoring apparatus: automated identification system (AIS) and vessel-pass (VPASS) information. Two different analyses regarding the vessel detection output utilization were, therefore, presented. Primary analysis demonstrated a vessel identification algorithm, comparing the vessel detection output with elaborately preprocessed AIS and VPASS information, which indicated the discrete position and velocity of vessel. The other presented a position restoration algorithm via i) velocity estimator complementing the conventional fractional Fourier transform velocity estimation analysis, while investigating the effect of range acceleration in deriving the azimuth velocity and ii) measuring the vessel orientation angle from Radon transform. Both algorithms were implemented to the vessel detection output in Cosmo-SkyMed SAR images, depicting an enhanced accuracy compared to the conventional algorithm both in velocity estimation and azimuth shift estimation; velocity offset reduced from 1.64 m/s to 1.29 m/s and average azimuth shift offset reduced from 70.75 m to 62.39 m. The presented algorithms would be decisive in terms of practicality if robustly attached to convolutional neural network-based vessel detection algorithms demonstrating ideal detection performances.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Machine Learning Methods for Spaceborne GNSS-R Sea Surface Height
           Measurement From TDS-1

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      Authors: Yun Zhang;Shen Huang;Yanling Han;Shuhu Yang;Zhonghua Hong;Dehao Ma;Wanting Meng;
      Pages: 1079 - 1088
      Abstract: Sea surface height (SSH) retrieval based on spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) usually uses the GNSS-R geometric principle and delay-Doppler map (DDM). The traditional method condenses the DDM information into a single scalar measure and requires error model correction. In this article, the idea of using machine learning methods to retrieve SSH is proposed. Specifically, two widely-used methods, principal component analysis combined with support vector regression (PCA-SVR) and convolution neural network (CNN), are used for verification and comparative analysis based on the observation data provided by Techdemosat-1 (TDS-1). According to the DDM inversion method, ten features from TDS-1 Level 1 data are selected as inputs; The SSH verification model based on the Danmarks Tekniske Universitet (DTU) 15 ocean wide mean SSH model and the DTU global ocean tide model is used as output verification of SSH. For the hyperparameters in the machine learning model, a grid search strategy is used to find the optimal values. By analyzing the TDS-1 data from 31 GPS satellites, the mean absolute error (MAE), root-mean-square error (RMSE) and coefficient of determination (R2) of the PCA-SVR inversion model are 0.61 m, 1.72 m, and 99.56%, respectively; and the MAE, RMSE, and R2 of the CNN inversion model is 0.71 m, 1.27 m, and 99.76%, respectively. In addition, the time required to train the PCA-SVR and CNN inversion models is also analyzed. Overall, the technique proposed in this article can be confidently applied to SSH inversion based on TDS-1 data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Attention-Based Octave Network for Hyperspectral Image Denoising

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      Authors: Ziwen Kan;Suhang Li;Mingzheng Hou;Leyuan Fang;Yi Zhang;
      Pages: 1089 - 1102
      Abstract: Inevitable corruption and degeneration make the performance of subsequent high-level semantic tasks in hyperspectral images (HSIs) unsatisfactory. Despite that many denoising methods have been proposed, significant room for improvement still remains. To better suppress noise and preserve the HSI spatial–spectral structure, we propose an attention-based Octave dense network. A separable spectral feature extraction module is introduced to extract the spatial–spectral features consistent with the structure prior. The extracted features are fine-tuned by the attention module in both channel and spatial domains; then, several dense denoising blocks are elaborately employed to focus on noise feature learning; in order to focus on high-frequency features, which usually have more noise information, we introduce the Octave kernel to implement these blocks. Experiments based on simulated and real-world noisy images demonstrate that the proposed method outperforms the existing traditional and learning-based methods in both quantitative evaluations and visual effects, benefiting the subsequent classification task. In addition, the effectiveness of each module is proven by ablation experiments. Our source code is made available at: https://github.com/LbzSteven/AODN.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Power and Performance Study of Compact L-Band Total Power Radiometers
           for UAV Remote Sensing Based in the Processing on ZYNQ and ARM
           Architectures

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      Authors: Daniel Ernesto Mera;Rafael A. Rodríguez Solís;Lorenzo Reyes;Roy Armstrong;William J. Hernandez;Alba L. Guzmán-Morales;
      Pages: 1103 - 1113
      Abstract: This article presents a study on the power consumption and performance analysis of a small, portable, and ultra-low power total power L-band radiometer. The article explores two processing architectures: the ZYNQ 7010 and the ARM A8 embedded microprocessor. The processing algorithm based in C++ was tested for different clock frequencies, ADC sampling speeds, and sizes of the ADC buffer. To reduce the power consumption and the algorithm execution time, high-level and system-level optimizations, along with fixed-point Q(16,16) data representation, were applied to the main code running on LINUX Debian V8. In the case with the ZYNQ 7010, the optimizations had no notable impact on reducing power or execution time in comparison with the ARM A8, where significant variations were measured, showing a tradeoff between power consumption and algorithm performance that limits the processing capability and the system flight time. The ZYNQ 7010 runs the algorithm faster, but the power consumption is higher than the ARM A8. Using the fixed-point Q(16,16) implementation reduced the power consumption and the execution time in both architectures. Based on these results, we developed a heuristic methodology to minimize power consumption and increase the performance. Energy consumption savings for the radiometer during 20 min of flight was 48%. The size of the radiometer was reduced to 30 cm × 30 cm × 10 cm, with a weight of 1.36 kg, (3 lb) allowing the system be carried by small drones. The results were validated measuring salinity at two locations in Western Puerto Rico.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Quantitative Analysis of Urban Polycentric Interaction Using Nighttime
           Light Data: A Case Study of Shanghai, China

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      Authors: Yue Tu;Zuoqi Chen;Congxiao Wang;Bailang Yu;Bingjie Liu;
      Pages: 1114 - 1122
      Abstract: The urban polycentric structure is connected to the economy and enormously impacts socioeconomic development and policies. Unlike traffic data and big geographic data, remote sensing data have shown an accessible way to measure urban spatial interaction. However, most existing studies only focused on the interaction among cities rather than within cities. Meanwhile, the urban spatial interaction, which should be directional, was always expressed as an undirected graph. Therefore, this article developed a network-based radiation model using nighttime light remote sensing data and mapped a directed interaction network (inward and outward direction) among urban centers. Taking the region within the outer ring of Shanghai as an example, the taxi trajectory data were adopted to validate the result with the R2 of 0.61. We discovered that: the urban polycentric interaction network is dumbbell-shaped with an east-west development corridor crossing the main center and connecting two main urban center clusters. The in-strength and out-strength interaction of each urban center have a similar distribution. The urban centers with higher in-strength and out-strength are mainly concentrated toward the main center, especially in the east-west direction. At the urban center level, the total inward interaction is slightly higher than the total outward interaction of most urban centers. Spatially, an unbalanced distribution was found. In summary, our proposed method effectively indicates the urban polycentric interaction and is applicable to other regions since it requires no arbitrary parameters and the input data (e.g., nighttime light data) is readily available.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in
           a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

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      Authors: Aubin Allies;Antoine Roumiguié;Rémy Fieuzal;Jean-François Dejoux;Anne Jacquin;Amanda Veloso;Luc Champolivier;Frédéric Baup;
      Pages: 1123 - 1138
      Abstract: This article investigates the potential of the assimilation of both synthetic aperture radar (SAR)-derived dry mass (DM) and optically derived green area index (GAI) in an agro-meteorological model (i.e., SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R2 = 0.84 and 0.92, respectively, and relative root mean square error, RMSEr = 41% and 28%, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or monosensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push toward the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SAR-derived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Ship Monitoring With Bistatic Compact HFSWR of Small Aperture

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      Authors: Yonggang Ji;Yiming Wang;Weifeng Sun;Ruifu Wang;Ming Li;Xiaoyu Cheng;Xu Liang;
      Pages: 1139 - 1149
      Abstract: High-frequency surface wave radar (HFSWR) can detect and continuously track ship targets in real time and beyond the horizon. Compared with transmit/receive (T/R) monostatic HFSWR, the T-R bistatic HFSWR has the advantages of flexibility, receiver concealment, and large coverage because of the separation between the radar transmitter and receiver locations. So far, there is little research on marine ship monitoring for bistatic HFSWR. This article examines ship monitoring performance with T-R bistatic compact HFSWR of small aperture based on measured experimental data. The first-order sea-clutter characteristics and detection blind zone for T-R bistatic HFSWR were investigated theoretically, and the formulas for the moving ships were derived and its position accuracy were analyzed from simulation results. The experimental results of ship target detection of bistatic compact HFSWR carried out in 2015 were presented, and the spectrum characteristics of the bistatic HFSWR were analyzed with measured experimental data. A method integrating detection and tracking was applied to the target detection data collected by the bistatic compact radar, and two targets examples tracked were given. Finally, the validity of the method and the tracing results were verified by using synchronous automatic identification system data, and the ship positioning accuracy of the bistatic compact HFSWR was statistically analyzed. The analysis shows that bistatic radars have larger errors than monostatic T/R radars, with a positioning error as large as 10 km.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • GCSANet: A Global Context Spatial Attention Deep Learning Network for
           Remote Sensing Scene Classification

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      Authors: Weitao Chen;Shubing Ouyang;Wei Tong;Xianju Li;Xiongwei Zheng;Lizhe Wang;
      Pages: 1150 - 1162
      Abstract: Deep convolutional neural networks have become an indispensable method in remote sensing image scene classification because of their powerful feature extraction capabilities. However, the ability of the models to extract multiscale features and global features on surface objects of complex scenes is currently insufficient. We propose a framework based on global context spatial attention (GCSA) and densely connected convolutional networks to extract multiscale global scene features, called GCSANet. The mixup operation is used to enhance the spatial mixed data of remote sensing images, and the discrete sample space is rendered continuous to improve the smoothness in the neighborhood of the data space. The characteristics of multiscale surface objects are extracted, and their internal dense connection is strengthened by the densely connected backbone network. GCSA is introduced into the densely connected backbone network to encode the context information of the remote sensing scene image into the local features. Experiments were performed on four remote sensing scene datasets to evaluate the performance of GCSANet. The GCSANet achieved the highest classification precision on AID and NWPU datasets and the second-best performance on the UC Merced dataset, indicating the GCSANet can effectively extract the global features of remote sensing images. In addition, the GCSANet presents the highest classification accuracy on the constructed mountain image scene dataset. These results reveal that the GCSANet can effectively extract multiscale global scene features on complex remote sensing scenes. The source codes of this method can be foundin https://github.com/ShubingOuyangcug/GCSANet.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Analysis on Effective UAS Survey Conditions for Classification of Coastal
           Sediments

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      Authors: Hyesu Kim;Jaehyung Yu;Lei Wang;Chanhyeok Park;Hyuk Soo Han;Seong-Geon Jang;
      Pages: 1163 - 1173
      Abstract: This study aims to introduce effective unmanned aerial system (UAS) survey conditions for coastal sediment classification, including muddy sand, sand, gravel, and shells in a tidal flat area. UAS images with resolutions ranging from 2 to 60 mm are used as an implication of survey altitudes. The UAS images are used for sediment classification using random forest (RF) and support vector machine (SVM) methods. The results showed that RF is more effective in sediment classification while the general accuracy pattern was similar. The accuracy decreased with lower spatial resolutions. Notably, there is a significant drop of accuracy with a resolution coarser than 40 mm. Considering the training data selection, classification accuracy, and survey efficiency, it is suggested that 40 mm UAS images would provide optimal condition with acceptable accuracy for coastal sediment classification using RF model. To gain higher accuracy, a lower flight altitude is required, which will elongate the survey time significantly. Given the fact that this study is the first approach to test various UAS survey conditions for coastal sediment classifications in a field condition; the methodology and findings of this study can serve as a guideline framework for future coastal UAS sediment mapping.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Feature Fusion Airport Detection Method Based on the Whole Scene
           Multispectral Remote Sensing Images

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      Authors: Xinyu Dong;Jia Tian;Qingjiu Tian;
      Pages: 1174 - 1187
      Abstract: Being one of the most important infrastructures, airports play a vital role in both civil fields and military fields. However, detect airports directly based on the whole scene remote sensing images (RSIs) with complex background remains challenging. To address this issue, this article proposes a method that mainly combines spectral features and geometric features of airports with concrete runways to detect multiple airports simultaneously from a whole scene multispectral image with medium-high spatial resolution and with comparatively few bands (contains blue, green, red, and near-infrared bands). Specifically, a decision tree algorithm was developed based on the analysis of spectral features to extract main concrete areas within the whole RSI. Then, the geometric features are used to aim at extracting the point marks of candidate airports. The influence of different image spatial resolutions of the proposed method is explored and the detection effect and processing efficiency of proposed method is verified based on whole scene RSIs with complex background. The analysis of experimental results shows that Sentinel-2 images is more suitable for airport detection than Gaofen-6 and Landsat-8 images based on the proposed method. In addition, the proposed method provides high-accuracy detection of category Ⅳ airports based on Sentinel-2 images with different background complexity in experimental areas indicate the proposed method has a high robust and a good applicability. Finally, run-time test of the proposed method was conducted, and it demonstrates the proposed method has the higher processing efficiency when applying to regional airport detection.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SPAN: Strong Scattering Point Aware Network for Ship Detection and
           Classification in Large-Scale SAR Imagery

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      Authors: Yuanrui Sun;Zhirui Wang;Xian Sun;Kun Fu;
      Pages: 1188 - 1204
      Abstract: Ship detection and classification in synthetic aperture radar (SAR) images play a vital role for wide applications. Due to the unique SAR imaging mechanism, ship detection and classification tasks have faced numerous challenges, such as land interference, image defocus, and noise. Many detectors and classifiers have been presented to handle these problems. However, the general deep learning-based detectors and classifiers lack the combination of SAR characteristics, which leads to poor performance. Compared with optical images, SAR images lack the texture information of ships, which brings great difficulties to the recognition task. To address the above issues, a novel deep learning-based ship detection and classification network combined with scattering characteristics is proposed in this article. First, to accurately locate ships in large-scale SAR images, this article designs a strong scattering point aware network (SPAN) by capturing the strong scattering points that existed in the ship area. SPAN recognizes the ship category according to their distribution characteristics. Second, to compensate for the feature loss caused by the down-sampling operation, this article designs a more suitable resolution recovery module to replace the bilinear interpolation method. Third, a region of interest automatic generation module is proposed to fully utilize the axis-align feature of oriented proposal boxes and the sufficient information of horizontal proposal boxes. Furthermore, the classification encoder module extracts the distribution feature of scattering points to classify SAR ships. Finally, the comprehensive experiments in the large-scale dataset for ship detection and classification in SAR images (LDSD) demonstrate the superior performance of the proposed method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Pseudo-Siamese Deep Convolutional Neural Network for Spatiotemporal
           Satellite Image Fusion

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      Authors: Weisheng Li;Chao Yang;Yidong Peng;Jiao Du;
      Pages: 1205 - 1220
      Abstract: Due to technology and cost limitations, it is challenging to obtain high temporal and spatial resolution images from a single satellite spectrometer, which significantly limits the specific application of such remote sensing images in earth science. To solve the problem that the existing algorithms cannot effectively balance the spatial detail preservation and spectral change reconstruction, a pseudo-Siamese deep convolutional neural network (PDCNN) for spatiotemporal fusion is proposed in this article. The method proposes a pseudo-Siamese network framework model for fusion. This framework has two independent and equal feature extraction streams, but the weights are not shared. The two feature extraction streams process the image information at the previous and later moments and reconstruct the fine image of the corresponding time to fully extract the image information at different times. In the feature extraction stream, the multiscale mechanism and dilated convolution of flexible perception are designed, which can flexibly obtain feature image information and improve the model reconstruction accuracy. In addition, an attention mechanism is introduced to improve the weight of the crucial information for the remote sensing images. Adding a residual connection enhances the reuse of the initial feature information in shallow networks and reduces the loss of feature information in deep networks. Finally, the fine images obtained from the two feature extraction streams are weighted and fused to obtain the final predicted image. The subjective and objective results demonstrate that the PDCNN can effectively reconstruct the fusion image with higher quality.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Coupling Dual Graph Convolution Network and Residual Network for Local
           Climate Zone Mapping

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      Authors: Yedan Yu;Jie Li;Qiangqiang Yuan;Qian Shi;Huanfeng Shen;Liangpei Zhang;
      Pages: 1221 - 1234
      Abstract: Local climate zone (LCZ) has become a new standard classification scheme in urban landscapes and showed great potential in urban climate research. Traditional classifiers and ordinary neural networks only consider the spectral or local spatial features of the pixel, ignoring the effect of nonlocal information on the LCZ classification. The graph convolutional network (GCN) has been used to exploit the relationship between adjacent and global land covers owing to the ability to conduct flexible convolution over graphs. In this work, we integrated a convolutional neural network and two GCNs into an end-to-end hybrid framework and generated LCZs directly from the original images. Local-, regional-, and global-level features were extracted and grouped complementarily to foster better performance. Experiments were conducted in six cities around the world to verify the effectiveness of our method. Results showed that the average classification accuracy of the six cities reached 0.956 and performed better than any other comparable model. Ablation experiments also demonstrated the mutual promotion of the different modules. Finally, the small sample experiment provided a practical reference for the LCZ classification in the absence of samples in future.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Augmentation of Vegetation Index Curves Considering the Crop-Specific
           Phenological Characteristics

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      Authors: P. V. Arun;Arnon Karnieli;
      Pages: 1235 - 1243
      Abstract: The state-of-the-art crop phenological classifiers use vegetation index (VI) time-series data and deep learning (DL) techniques. However, the scarcity of training samples limits the performance of these approaches. Unlike the conventional augmentation techniques, the data augmentation of VI curves should preserve the crop-specific phenological events. The DL-based augmentation approaches do not give good results when the training samples are limited. Also, the conventional approaches such as translation, rotation, scaling, and wrapping do not preserve the characteristic features of the index curves, thereby making them inappropriate for the VI-curve-based augmentations. This article proposes a non-DL-based data augmentation strategy that requires only a minimal number of actual training samples. In the proposed approach, the periodic phenological events and the underlying trend for each crop class are modeled to improve the augmentation. The trends of different crop classes are estimated by jointly maximizing the autocorrelation and variance, while the optimal subsequences are generalized as the phenological events. The proposed augmentation strategy of using Maximal overlap discrete wavelet transform for obtaining the surrogates that retain the crop-specific features and periodicities significantly improves the results. It may be noted that the proposed approach does not alter the wavelet coefficients that are characteristics of a given crop class. The experiments using time series VI data, covering 90 fields of wheat, and 60 fields of barley, confirm better accuracy of the proposed augmentation approaches as compared to the prominent approaches.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Low-Power, Ambiguous Synthetic Aperture Radar Concept for Continuous
           Ship Monitoring

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      Authors: Nertjana Ustalli;Gerhard Krieger;Michelangelo Villano;
      Pages: 1244 - 1255
      Abstract: This article addresses the design of a low-cost synthetic aperture radar (SAR) for a single dedicated application, namely illegal vessel detection, which can be implemented using a small satellite and is characterized by reduced transmit power and high resolution. Minimum requirements in terms of noise equivalent sigma zero and resolution that ensure acceptable detection performance are derived on the basis of ship statistics extracted from TerraSAR-X data. One peculiarity of the design is that a pulse repetition frequency much smaller than the nominal Doppler bandwidth is selected to increase the swath width beyond the classical SAR limitation without using digital beamforming, as the azimuth ambiguities can be tolerated for this specific application. Several design examples of SAR systems operating in X-band demonstrate the potential of this concept for small ship monitoring over swaths of 50–90 km with antennas smaller than 0.6 m2 and very low average transmit powers comprised between 20 and 80 W.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SNR Improvement for Maneuvering Ship Using Weak Echo Under the Condition
           of Beidou GEO Satellites

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      Authors: Yan Li;Songhua Yan;Jianya Gong;
      Pages: 1256 - 1271
      Abstract: Using global navigation satellite systems as transmitters of opportunity for target detection is a hot issue. The problems of this technology are the restricted power budget provided by navigation satellites and the defocusing caused by the movement of target. To refocus the echo and improve the signal-to-noise ratio under the condition of Beidou Geostationary satellites, this article uses jointly the short-time coherent integration and the long-time integration based on Fractional Fourier transform to correct the range migration and the Doppler frequency migration of signals for refocusing the signal energy. The effectiveness of the proposed algorithm for refocusing the echo from different types of maneuvering ships (a cargo ship and a ferry) is confirmed via an experimental campaign.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Investigating the Intra-Annual Dynamics of Kunlun Glacier in the West
           Kunlun Mountains, China, From Ascending and Descending Sentinel-1 SAR
           Observations

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      Authors: Yueling Shi;Guoxiang Liu;Xiaowen Wang;Qiao Liu;C. K. Shum;Jiawen Bao;Wenfei Mao;
      Pages: 1272 - 1282
      Abstract: Monitoring glacier dynamics is an effective approach for quantifying the response of the glaciers in cold arid mountainous areas to climate change. However, the quantification of short-term flow dynamics of the mountain glaciers in cold-dry climates has rarely been reported. This article investigated the intra-annual flow dynamics in terms of velocity and ice thickness changes on the Kunlun glacier, a mountain glacier in the cold-dry west Kunlun mountains, using spaceborne synthetic aperture radar (SAR) imagery. We applied the improved pixel-offset-tracking small-baseline-subset method (PO-SBAS) on ascending and descending Sentinel-1A SAR images acquired in 2017 and 2018 to estimate the three-dimensional (i.e., north–south, west–east, and vertical) velocity time series of the glacier. The vertical velocities were further decomposed into the surface-parallel-flow (SPF) and the nonsurface-parallel-flow (nSPF) components, which link glacier motion along glacier surface slope and internal ice deformation, respectively, to glacier thickness changes. Our findings show that the eastern branch of the glacier moved faster than the western branch. We inferred that a loss of ice thickness due to a previous surge on the western branch should be responsible for its slower flow. The nSPF rates are higher than the SPF rates in both branches, indicating that internal ice deformation primarily controls the changes in ice thickness. We also observed an apparent summer acceleration in the nSPF rates, which is likely caused by changes in subglacial hydrological conditions. This article highlights the potential uses of the improved PO-SBAS method of quantifying the flow dynamics of the glaciers in cold-dry mountain regions.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An Operational Land Surface Temperature Retrieval Methodology for Chinese
           Second-Generation Huanjing Disaster Monitoring Satellite Data

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      Authors: Enyu Zhao;Caixia Gao;Qijin Han;Yuying Yao;Yulei Wang;Chunyan Yu;Haoyang Yu;
      Pages: 1283 - 1292
      Abstract: The Chinese second-generationHuanjing disaster monitoring satellite (HJ-2A) was launched on September 27, 2020, and underutilized due to the lack of accurate operational methodologies for land surface temperature (LST) retrieval.In this article, an operational LST retrieval method is proposed to retrieve LSTs from HJ-2A thermal infrared observations. The LST retrieval methodology involves two main steps. The land surface emissivities (LSEs) over all land cover types are obtained with the improved normalized difference vegetation index-based threshold method, and then the LST is retrieved operationally from the adjacent infrared bands.The algorithm coefficients for LST retrieval are from regression analysis of radiative transfer simulations, and LSTs could be retrieved based on thermal images without any additional auxiliary data. The simulation results demonstrated that the root-mean-square errors (RMSEs) of LST retrieval were less than 2.4 K in all subranges, and the minimum RMSE for the two emissivity groups (high- (low-) emissivity group) was 0.16 K (0.20 K) and appeared in the tractable subrange with water vapor content (WVC) varying from 0 to 1.5 g/cm2 and view zenith angle (VZA) being 0°. Furthermore, an error analysis was performed, the results showed that the LSE, NEΔT, and atmospheric water vapor uncertainty of 1%, 0.2 K, and 20% caused the LST retrieval errors with 0.88–1.21 K (0.84–1.19 K), 0.1 K (0.09 K), and 0.006 K (0.008 K) for the high- (low-) emissivity group, respectively, with WVC∊[0–1.5] g/cm2 and VZA = 0°. Finally, the retrieved LSTs were applied to seven images of the Wuhai, Geermu, Dunhuang, and Baotou sites from January to March and cross validated by the moderate resolution imaging spectroradiom-ter (MODIS) LST products. From the cross-validated results, it can be found that the RMSEs of the retrieved LSTs and the MODIS LST products were between 2.3 and 3.7 K, and the mean RMSE value was 2.89 K.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • ORTP: A Video SAR Imaging Algorithm Based on Low-Tubal-Rank Tensor
           Recovery

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      Authors: Wei Pu;Junjie Wu;Yulin Huang;Jianyu Yang;
      Pages: 1293 - 1308
      Abstract: Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Simulated Geophysical Noise in Sea Ice Concentration Estimates of Open
           Water and Snow-Covered Sea Ice

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      Authors: Rasmus T. Tonboe;Vishnu Nandan;Marko Mäkynen;Leif Toudal Pedersen;Stefan Kern;Thomas Lavergne;Johanne Øelund;Gorm Dybkjær;Roberto Saldo;Marcus Huntemann;
      Pages: 1309 - 1326
      Abstract: Sea ice concentration algorithms using brightness temperatures ($T_{B}$) from satellite microwave radiometers are used to compute sea ice concentration ($c_{text{ice}}$), sea ice extent, and generate sea ice climate data records. Therefore, it is important to minimize the sensitivity of $c_{text{ice}}$ estimates to geophysical noise caused by snow/sea ice thermal microwave emission signature variations, and presence of WV and clouds in the atmosphere and/or near-surface winds. In this study, we investigate the effect of geophysical noise leading to systematic $c_{text{ice}}$ biases and affecting $c_{text{ice}}$ standard deviations (STD) using simulated top of the atmosphere $T_{B}$s over open water and 100% sea ice. We consider three case studies for the Arctic and the Antarctic and eight different $c_{text{ice}}$ algorithms, representing different families of algorithms based on the selection of channels and methodologies. Our simulations show that, over open water and low $c_{text{ice}}$, algorithms using gradients between V-polarized 19-GHz and 37-GHz $T_{B}$s show the lowest sensitivity to the geophysical noise, while the algorithms exclusively using near-90-GHz channels have by far the highest sensitivity. Over sea ice, the atmosphere plays a much smaller role than over open water, and the $c_{text{ice}}$ STD for all algorithms is smaller than over open water. The hybrid and low-frequency (6 GHz) algorithms have the lowest sensitivity to noise over sea ice, while the polarization type of algorithms has the highest noise levels.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Incremental SAR Automatic Target Recognition With Error Correction and
           High Plasticity

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      Authors: Jiaxin Tang;Deliang Xiang;Fan Zhang;Fei Ma;Yongsheng Zhou;HengChao Li;
      Pages: 1327 - 1339
      Abstract: Synthetic aperture radar automatic target recognition (SAR ATR) uses computer processing capabilities to infer the classes of the targets without human intervention. For SAR ATR, deep learning gradually emerges as a powerful tool and achieves promising performance. However, it faces serious challenges of how to deal with incremental recognition scenarios. The existing deep learning-based SAR ATR methods usually predefine the total number of recognition classes. In realistic applications, the new tasks/classes will be added continuously. If all old data are stored and mixed with newly added data to update the model, the storage pressure and time consumption make the application infeasible. In this article, the high plastic error correction incremental learning (HPecIL) is proposed to address the model degradation and plasticity decline in the incremental scenario. Multiple optimal models trained on old tasks are used to correct accumulative errors and alleviate model degradation. Moreover, the sharp data distribution shift due to newly added data can also result in the model underperforming. A class-balanced training batch is constructed to deal with the issue of unbalanced data distribution. To make a tradeoff between model stability and model plasticity, low-effect nodes in the model are removed to boost the efficiency of model update. The proposed HPecIL outperforms the other state-of-the-art methods in incremental recognition scenarios. The experimental results demonstrate the effectiveness of the proposed method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Blind Hyperspectral Unmixing Using Autoencoders: A Critical Comparison

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      Authors: Burkni Palsson;Johannes R. Sveinsson;Magnus O. Ulfarsson;
      Pages: 1340 - 1372
      Abstract: Deep learning (DL) has heavily impacted the data-intensive field of remote sensing. Autoencoders are a type of DL methods that have been found to be powerful for blind hyperspectral unmixing (HU). HU is the process of resolving the measured spectrum of a pixel into a combination of a set of spectral signatures called endmembers and simultaneously determining their fractional abundances in the pixel. This article details the various autoencoder architectures used in HU and provides a critical comparison of some of the existing published blind unmixing methods based on autoencoders. Eleven different autoencoder methods and one traditional method will be compared in blind unmixing experiments using four real datasets and four synthetic datasets with different spectral variability. Additionally, extensive ablation experiments with a simple spectral unmixing autoencoder will be performed. The results are interpreted in terms of the various implementation details, and the question of why autoencoder methods are so powerful compared to traditional methods is unraveled. The source codes for all methods implemented in this article can be found at https://github.com/burknipalsson/hu_autoencoders.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With
           Transformers

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      Authors: Tai An;Xin Zhang;Chunlei Huo;Bin Xue;Lingfeng Wang;Chunhong Pan;
      Pages: 1373 - 1388
      Abstract: Multiimage super-resolution (MISR), as one of the most promising directions in remote sensing, has become a needy technique in the satellite market. A sequence of images collected by satellites often has plenty of views and a long time span, so integrating multiple low-resolution views into a high-resolution image with details emerges as a challenging problem. However, most MISR methods based on deep learning cannot make full use of multiple images. Their fusion modules are incapable of adapting to an image sequence with weak temporal correlations well. To cope with these problems, we propose a novel end-to-end framework called TR-MISR. It consists of three parts: An encoder based on residual blocks, a transformer-based fusion module, and a decoder based on subpixel convolution. Specifically, by rearranging multiple feature maps into vectors, the fusion module can assign dynamic attention to the same area of different satellite images simultaneously. In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent. TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches. Extensive experiments performed on the PROBA-V Kelvin dataset demonstrate the superiority of the proposed model that provides an effective method for transformers in other low-level vision tasks.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Automatic Segmentation of Individual Grains From a Terrestrial Laser
           Scanning Point Cloud of a Mountain River Bed

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      Authors: Agata Walicka;Norbert Pfeifer;
      Pages: 1389 - 1410
      Abstract: In this article, we propose a method for instance segmentation of individual grains from a terrestrial laser scanning point cloud representing a mountain river bed. The method was designed as a classification followed by a segmentation approach. The binary classification into either points representing river bed or grains is performed using the random forest algorithm. The point cloud is classified based only on geometrical features calculated for a local, spherical neighborhood. A multisize neighborhood approach was used together with the feature selection method that is based on correlation analysis. The final classification was performed using a set of features calculated for the neighborhood size of 5, 15, and 20 cm. The achieved classification results have the overall accuracy of 85–95%, depending on the test site. The segmentation is performed using the density-based spatial clustering of applications with noise algorithm in order to cluster the point cloud based on Euclidean distances between points. The performed experiments showed that the proposed method enables us to correctly delineate 67–88% of grains, depending on the test site. However, the resulting point cloud based completeness expressed as Jaccard index is similar for each of the test sites and is approximately 88%. Moreover, the proposed method proved that it is robust to the shadowing effect.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Snowfall Detection Algorithm for ATMS Over Ocean, Sea Ice, and Coast

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      Authors: Yalei You;Huan Meng;Jun Dong;Yongzhen Fan;Ralph R. Ferraro;Guojun Gu;Likun Wang;
      Pages: 1411 - 1420
      Abstract: This study developed a snowfall detection algorithm over ocean, sea ice, and coast for the advanced technology microwave sounder (ATMS) onboard both NPP and NOAA-20 satellites. The detection algorithm was trained from collocated observations between ATMS-NPP and CloudSat cloud profiling radar (CPR) snowfall product. Both brightness temperature (TB) variables and global forecast system (GFS) output variables are evaluated for snowfall detection in this algorithm. Results show that combining TB variables and GFS variables provide the optimal snowfall detection performance. The Heidke skill score (HSS) values are about 0.56 over all three surface types, and the probability of detection (POD) values are 0.76, 0.70, and 0.72 over ocean, sea ice, and coast, respectively. The importance of the GFS variables differs greatly among these three surface types. The detection algorithm primarily depends on TB variables over ocean and HSS only increased by 0.05 by adding GFS variables. In contrast, GFS variables are critically important to snowfall detection over sea ice and coastal regions. Without GFS variables, the HSS values over both sea ice and coastal regions decrease sharply from about 0.56 to about 0.40. Over ocean, we also developed a regional snowfall detection model in each 10° grid box, which greatly outperform the global detection model over certain regions (e.g., sea of Okhotsk and Labrador Sea). Case studies and validation against NOAA-20 observations showed that the snowfall detection algorithm performs well, which will benefit coastal communities by providing information on snowstorms offshore before they transition to land.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

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      Authors: Meisam Amani;Farzane Mohseni;Nasir Farsad Layegh;Mohsen Eslami Nazari;Farzam Fatolazadeh;Abbas Salehi;Seyed Ali Ahmadi;Hamid Ebrahimy;Arsalan Ghorbanian;Shuanggen Jin;Sahel Mahdavi;Armin Moghimi;
      Pages: 1421 - 1453
      Abstract: As discussed in the previous part of this review article, remote sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review article was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, synthetic aperture radar, light detection and ranging, sound navigation and ranging, high-frequency radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review article provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multiview Hierarchical Network for Hyperspectral and LiDAR Data
           Classification

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      Authors: Yishu Peng;Yuwen Zhang;Bing Tu;Chengle Zhou;Qianming Li;
      Pages: 1454 - 1469
      Abstract: In recent times, multisource remote sensing technology [e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data] has been widely used in urban land-use recognition owing to its high classification effectiveness compared to using only single-source data. In this study, a multiview hierarchical network (MVHN) technique is developed for HSI and LiDAR data classification, which conducts the following execution procedures. First, based on the a preset band step length, the original HSI is sampled and divided into multiple groups with exactly the same number of bands to obtain spectral features. Then, principal components analysis is performed on the raw HSI to extract the first principal components (PCs) that meet the size of the LiDAR image. The Gabor filters are applied to the PCs and LiDAR to capture spatial details (i.e., textural features) of scenes. Specifically, a stacking mechanism is employed to generate fusion features once the above features are available. Next, a three-dimensional ResNet-like deep CNN is designed to extract the spectral–spatial information of the fusion feature. Finally, majority-voting is introduced into the classification results of the network trained using each fusion feature to achieve high-confidence final results. Experiments on three well-known HSI and LiDAR datasets (i.e., Houston, MUUFL, and Trento datasets) demonstrate the effectiveness of the proposed MVHN method compared to state-of-the-art comparable classification methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Symmetric Information–Theoretic Metric Learning for Target
           Detection in Hyperspectral Imagery

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      Authors: Yanni Dong;Yuxiang Zhang;Bo Du;
      Pages: 1470 - 1480
      Abstract: Metric learning-based methods, which yield great performance and show considerable potential to improve the performance of hyperspectral image processing, aim to calculate the Mahalanobis distance metric matrix. In this article, we proposed a symmetric information-theoretic metric learning (SITML) method for hyperspectral target detection. The SITML algorithm is designed based on the classical information-theoretic metric learning (ITML) and, minimizes the differential Kullback–Leibler (KL) divergence. To enhance both of the detection performance and the generalization ability, we build metric spaces from the neighborhood of training samples to preserve the local discriminative information. Then, we conduct local pairwise constraints to maximize the Jeffery divergence (also named the symmetric KL divergence) of two multivariate Gaussian distributions to solve the problem of an asymmetric KL divergence. Finally, we use a closed-form solution to solve the optimization problem. Intensive experiments on three hyperspectral datasets indicate that SITML outperforms the classical ITML algorithm and other state-of-the-art target detection methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Video SAR Moving Target Tracking Using Joint Kernelized Correlation Filter

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      Authors: Chao Zhong;Jinshan Ding;Yuhong Zhang;
      Pages: 1481 - 1493
      Abstract: Video synthetic aperture radar (ViSAR) has been found very useful for the surveillance of ground moving targets. The target energy can be utilized for ground moving target tracking, while the dynamic shadows of moving targets enable an alternative tracking approach. However, neither of these two approaches can stand alone to provide reliable target tracking. The smeared shadow and energy both degrade the tracking performance when the target is maneuvering. A moving target tracking framework based on the joint kernelized correlation filter (JKCF) has been developed. Based on the feature training of JKCF, the target is tracked by combining its shadow in the sequential SAR imagery and the corresponding energy in the range-Doppler (RD) spectra. Aiming at the problems of tracking drift and collapse, interactive processing is adopted to enhance the target positioning and feature update based on the confidence assessment. By cooperating with the initialization and feature update strategy, the tracking success rate and precision can be improved significantly.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Res2-Unet, a New Deep Architecture for Building Detection From High
           Spatial Resolution Images

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      Authors: Fang Chen;Ning Wang;Bo Yu;Lei Wang;
      Pages: 1494 - 1501
      Abstract: Accurate large-scale building detection is significant in monitoring urban development, map updating, change detection, and digital city establishment. However, due to the complicated details of background objects in high spatial resolution remotely sensed images, the models proposed in building detection are still not performing satisfactorily. Particularly, such issue lies in the small buildings, which are easily to be omitted, and the pixels in the bounding area of each building instance can be especially confusing with the background objects. Aiming to deal with such problem, we propose Res2-Unet to employ multi-scale learning at a granular level, rather than the commonly used layer-wise feature learning, to enlarge the scale of receptive fields of each bottleneck layer. It replaces the widely used 3 $ times $ 3 convolution on n-channel feature maps with a set of smaller groups, which are organized in a hierarchical structure to enlarge the scale-variability. The general framework is an end-to-end learning network, taking a typical semantic segmentation network structure with encoders to encode the input image into feature maps and decoders to decode the feature maps into binary segmented result image. Moreover, to enhance the building boundary generation ability of our model, a boundary loss function is proposed to improve the detection performance. The proposed framework is evaluated on three public datasets, Massachusetts building dataset, WHU East Asia Satellite dataset and WHU Aerial building dataset. It is compared with the published performances and has achieved the state-of-the-art accuracies. That verifies the robustness of the proposed framework.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Accounting for Label Errors When Training a Convolutional Neural Network
           to Estimate Sea Ice Concentration Using Operational Ice Charts

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      Authors: Manveer Singh Tamber;K. Andrea Scott;Leif Toudal Pedersen;
      Pages: 1502 - 1513
      Abstract: Convolutional neural networks (CNNs) are being increasingly investigated as a means to extract sea ice concentration from synthetic aperture radar (SAR) in an automated manner. This is often done using ice charts as training data. However, in these charts, an ice concentration label is given to a large region, which may not have a spatially uniform sea ice concentration distribution at the prediction scale of the CNN. This leads to representativity errors, which can be more pronounced at intermediate sea ice concentrations. In this study, we first investigate ways to perturb the ice chart labels to obtain improved predictions to account for the label uncertainty for intermediate ice concentrations. We then propose a method to augment the ice chart data by rescaling the information in the SAR imagery. The method is found to lead to improved accuracy in comparison to using the ice chart labels alone, with accuracy improving from 0.919 to 0.966. The sea ice concentration maps with the augmented labels also have much finer detail than the other approaches evaluated. These details are visually in agreement with expected sea ice concentration from the SAR data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An Accurate Calculation of the Atmospheric Refraction Error of Optical
           Remote Sensing Images Based on the Fine-Layered Light Vector Method

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      Authors: Jiang Ye;Haiqing He;Ling Zhang;Xu Lin;Yuxuan Qiang;
      Pages: 1514 - 1525
      Abstract: Atmospheric refraction is an important factor that affects the positioning accuracy of optical high-resolution satellite imaging. Current calculation methods do not consider regional change characteristics in the atmosphere; the atmospheric division model is rough and does not conform to reality. This article studies atmospheric refraction in optical remote sensing satellite observations, provides a formula for calculating the strict refraction of a multilayer atmosphere, and shows a new method for calculating atmospheric refraction error. We use a ray-tracing method to calculate the light propagation path in the atmosphere and continuously layer the atmosphere according to the height difference of 100 m. Different from general calculation methods, we prove that a regional atmospheric model can calculate the atmospheric refraction index more accurately than using the empirical model. As the off-nadir angle gradually increases, the calculation results in this article are better than the current commonly used methods. We use WorldView-2 images of the Qinghai–Tibet Plateau region in China for the experiments. When the off-nadir angle is less than 32°, the positioning accuracy improves by 6–47%. Compared with the standard atmospheric model, the regional atmospheric model improves positioning accuracy by 2–18%. This method reflects the continuous variation in the atmospheric refractive index with the vertical distribution of the atmosphere and amended regional meteorological conditions. Model errors caused by overly simple atmospheric division are avoided, andthe positioning accuracy of optical remote sensing images is increased.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • CGSANet: A Contour-Guided and Local Structure-Aware Encoder–Decoder
           Network for Accurate Building Extraction From Very High-Resolution Remote
           Sensing Imagery

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      Authors: Shanxiong Chen;Wenzhong Shi;Mingting Zhou;Min Zhang;Zhaoxin Xuan;
      Pages: 1526 - 1542
      Abstract: Extracting buildings accurately from very high-resolution (VHR) remote sensing imagery is challenging due to diverse building appearances, spectral variability, and complex background in VHR remote sensing images. Recent studies mainly adopt a variant of the fully convolutional network (FCN) with an encoder–decoder architecture to extract buildings, which has shown promising improvement over conventional methods. However, FCN-based encoder–decoder models still fail to fully utilize the implicit characteristics of building shapes. This adversely affects the accurate localization of building boundaries, which is particularly relevant in building mapping. A contour-guided and local structure-aware encoder–decoder network (CGSANet) is proposed to extract buildings with more accurate boundaries. CGSANet is a multitask network composed of a contour-guided (CG) and a multiregion-guided (MRG) module. The CG module is supervised by a building contour that effectively learns building contour-related spatial features to retain the shape pattern of buildings. The MRG module is deeply supervised by four building regions that further capture multiscale and contextual features of buildings. In addition, a hybrid loss function was designed to improve the structure learning ability of CGSANet. These three improvements benefit each other synergistically to produce high-quality building extraction results. Experimental results on the WHU and NZ32km2 building datasets demonstrate that compared with the tested algorithms, CGSANet can produce more accurate building extraction results and achieve the best intersection over union value 91.55% and 90.02%, respectively. Experiments on the INRIA building dataset further demonstrate the ability for generalization of the proposed framework, indicating great practical potential.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Exploration of a Future NOAA Infrared Sounder in Geostationary Earth Orbit

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      Authors: Flavio Iturbide-Sanchez;Zhipeng Wang;Satya Kalluri;Yong Chen;Erin Lynch;Murty Divakarla;Changyi Tan;Tong Zhu;Changyong Cao;
      Pages: 1543 - 1561
      Abstract: A geostationary (GEO) hyperspectral infrared sounder (HyIRS) is capable of providing high spectral (0.625 cm−1), temporal (every 30 min) and spatial (4 km) resolution observations over the continental U.S. (CONUS). Frequent observations from a GEO-HyIRS at high spatial resolution are expected to contribute to the generation of three-dimensional structures of atmospheric temperature and humidity, and wind. These new observations will provide valuable information for timely forecasts of severe storms over the CONUS and the overall Western Hemisphere. Infrared (IR) sounder observations from a geostationary orbit open a new set of possibilities, including the capability of monitoring the diurnal cycle of atmospheric patterns, which is difficult from Low Earth Orbit IR sounders and the capability of timely and accurate retrievals of several trace gases. In this article, the feasibility of adding a HyIRS into the next generation of U.S. geostationary environmental satellites is studied. The configuration of a notional U.S. GEO-HyIRS sensor and its ground data processing system are discussed. A hyperspectral IR data simulator is developed and reported as part of this engineering study, where proxy data is used to model the end-to-end ground processing system. Various considerations for the configuration and the calibration and validation of the instrument are addressed.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • CFNet: A Cross Fusion Network for Joint Land Cover Classification Using
           Optical and SAR Images

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      Authors: Wenchao Kang;Yuming Xiang;Feng Wang;Hongjian You;
      Pages: 1562 - 1574
      Abstract: As two of the most widely used remote sensing images, optical and synthetic aperture radar (SAR) images show abundant and complementary information on the same target owing to their individual imaging mechanisms. Consequently, using optical and SAR images simultaneously can better describe the inherent features of the target, and thus, be beneficial for subsequent remote sensing applications. In this article, we propose a novel modular fully convolutional network model to improve the accuracy of land cover classification by fully exploiting the complementary features of the two sensors. We investigate where and how to fuse the two images in the joint classification network. A cross-gate module with a bidirectional information flow is proposed to achieve the best fusion performance. In addition, to validate the proposed model, we construct a multiclass land cover classification dataset. Exhaustive experiments show that the proposed joint classification network presents superior results than state-of-the-art classification models using single-sensor images.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • WH-MAVS: A Novel Dataset and Deep Learning Benchmark for Multiple Land Use
           and Land Cover Applications

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      Authors: Jingwen Yuan;Lixiang Ru;Shugen Wang;Chen Wu;
      Pages: 1575 - 1590
      Abstract: Over the past decade, many excellent data sharing efforts have enriched the remote sensing scene classification (SC) methods. These datasets have achieved great success in complex high-level semantic information interpretation. However, most existing datasets are collected from standard and ungeoreferenced image patches for algorithm training and evaluation. These datasets do not fit for practical applications and cannot be directly applied in further geographical study. Accordingly, we provide a large range high-resolution SC dataset with multiple time phases, called “Wuhan Multiapplication VHR Scene classification dataset (WH-MAVS).” It facilitates the study of SC and scene change detection (SCD) algorithms. Moreover, it can also be directly employed to perform a variety of real-life land use application tasks. To the best of our knowledge, this is the first free, publicly available, georeferenced, and annotated dataset to cover almost an entire megacity. The WH-MAVS was collected and annotated from Google Earth imagery with the same spatial resolution and uniform nonoverlapping patch size, covering the central area of Wuhan, China. The total number of scene samples is 47 137, which belong to 14 classes with 23 567 labeled patches for each time phase in 2014 and 2016, respectively. The geographic coordinates of all samples in both time phases exhibit one-to-one correspondence with 23 202 unchanged image patches of scene categories and 365 changed ones. The distribution of the number of samples in each class is highly imbalanced; moreover, there are large intraclass differences and indistinguishable interclass variances. These characteristics are closer to the real land use/land cover application tasks and introduce further challenges to the related algorithm research. In addition, we conducted benchm-rk experiments on SC and SCD based on the WH-MAVS dataset with widely used deep learning models. DenseNet169 was found to achieve the best performance. The overall accuracies are 91.07% and 92.09%, respectively, in the 2014 and 2016 validation sets of WH-MAVS. Furthermore, SCD obtained by DenseNet169 has a binary change detection accuracy of 89.56% and a multiple (from–to) change detection accuracy of 86.70%. Over and above the research value of the algorithm, it is also proven to have practical applications in fields such as urban planning, landscape pattern analysis, and urban dynamic monitoring and analysis.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • JAGAN: A Framework for Complex Land Cover Classification Using Gaofen-5
           AHSI Images

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      Authors: Weitao Chen;Shubing Ouyang;Jiawei Yang;Xianju Li;Gaodian Zhou;Lizhe Wang;
      Pages: 1591 - 1603
      Abstract: Owing to their powerful feature extraction capabilities, deep learning-based methods have achieved significant progress in hyperspectral remote sensing classification. However, several issues still exist in these methods, including a lack of hyperspectral datasets for specific complicated scenarios and the need to improve the classification accuracy of land cover with limited samples. Thus, to highlight and distinguish effective features, we propose a hyperspectral classification framework based on a joint channel-space attention mechanism and generative adversarial network (JAGAN). To relearn feature-based weights, a higher priority was assigned to important features, which was developed by integrating a two-joint channel-space attention model to obtain the most valuable feature via the attention weight map. Additionally, two classifiers were designed in JAGAN: sigmoid was used to determine whether the input data were real or fake samples produced by the generator, while Softmax was adopted as a land cover classifier to yield the prediction type labels of the input samples. To test the classification performance of the JAGAN model, we used a self-constructed complex land cover dataset based on GaoFen-5 AHSI images, which consists of mixed landscapes of mining and agricultural areas from the urban-rural fringe. Compared with other methods, the proposed model achieved the highest overall classification accuracy of 86.09%, the highest kappa amount of 79.41%, the highest F1 score of 85.86%, and the highest average accuracy of 82.30%, indicating the JAGAN can effectively improve the classification accuracy for limited samples in complex regional environments using GF-5 AHSI images.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Investigating the Efficacy of the SMAP Downscaled Soil Moisture Product
           for Drought Monitoring Based on Information Theory

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      Authors: Zemian Wu;Jianxiu Qiu;Wade T. Crow;Dagang Wang;Zhengang Wang;Xiaohu Zhang;
      Pages: 1604 - 1616
      Abstract: Soil moisture (SM) information can be routinely obtained from high-quality microwave retrievals at a global scale—such as datasets generated by the Soil Moisture Active Passive (SMAP) mission. In this article, using mutual information (MI) theory, we investigate the efficacy of the downscaled SMAP/Sentinel-1 L2 3-km EASE-Grid SM product (SPL2) for the detection of agricultural drought over northwestern China. The SPL2 is generated by merging SMAP enhanced radiometer data with Sentinel-1 radar observations. To evaluate the efficiency of the SPL2 downscaled algorithm, the SMAP Enhanced L3 Radiometer 9-km EASE-Grid SM product (SPL3) is also utilized as a non-downscaled baseline. Over croplands, comparing normalized MI (NMI) values sampled between the NDVI time series and 3-km Sentinel-1 C-band backscatter coefficient (σ) from SPL2 with NMI values between NDVI and SPL3 radiometer brightness temperature (Tb; resampled to 3-km resolution), we find that the Sentinel-1 σ explains more (3-km) NDVI information than the SPL3 Tb, as the NMI between σvh (σvv) and NDVI is 15% (8%), larger than that between SPL3 Tb and NDVI (5%). However, compared to the SPL3 Tb baseline, the information from downscaled SPL2 Tb on NDVI is reduced by approximately 3%, and the SPL2 algorithm extracts only 7% (10%) of the total information available from both enhanced SPL3 Tb and Sentinel-1 σvh (σvv). Overall, the C-band σ signal provides valuable information for vegetation monitoring due to its frequency advantage- However, additional efforts should be focused on SPL2 merging algorithms to improve the value of the downscaled SPL2 product for agricultural applications.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multiscale Densely Connected Attention Network for Hyperspectral Image
           Classification

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      Authors: Xin Wang;Yanguo Fan;
      Pages: 1617 - 1628
      Abstract: Hyperspectral image classification (HSIC) based on deep learning has always been a research hot spot in the field of remote sensing. However, most of the classification models extract relevant features based on fixed-scales convolution kernels, which ignores the complex features of hyperspectral images (HSIs) at different scales and impairs the classification accuracy. To solve this problem, a multiscale densely connected attention network (MSDAN) is proposed for HSIC. First, the model adopts three different scales modules with dense connection to enhance classification performance, strengthen feature reuse, prevent overfitting and gradient disappearance. Besides, in order to reduce the model parameters and strengthen the extraction of spatial–spectral features, the traditional three-dimensional convolution is replaced by three-dimensional spectral convolution block and three-dimensional spatial convolution block. Furthermore, the spectral–spatial–channel attention is embedded into the end of each scale to enhance the favorable features for classification and further extract the discriminant features of the corresponding scale. Finally, the key feature extraction module is developed to extract multiscale fusion features to further enhance the classification performance of the network. The experimental results carried out on real HSIs show that the proposed MSDAN architecture has significant advantages compared with other most advanced methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Improved Pseudomasks Generation for Weakly Supervised Building Extraction
           From High-Resolution Remote Sensing Imagery

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      Authors: Fang Fang;Daoyuan Zheng;Shengwen Li;Yuanyuan Liu;Linyun Zeng;Jiahui Zhang;Bo Wan;
      Pages: 1629 - 1642
      Abstract: Benefiting from free labeling pixel-level samples, weakly supervised semantic segmentation (WSSS) is making progress in automatically extracting building from high-resolution (HR) remote sensing (RS) imagery. For WSSS methods, generating high-quality pseudomasks is crucial for accurate building extraction.To improve the performance of generating pseudomasks by using image-level labels, this article proposes a weakly supervised building extraction method by combining adversarial climbing and gated convolution. The proposed method optimizes class activation maps (CAMs) by using adversarial climbing strategy, generates accurate class boundary maps by introducing a gated convolution module, and further refines building pseudomasks by fusing pairing semantic affinities and CAMs with a random walk strategy. Experimental results on three datasets—two ISPRS datasets and a self-annotated dataset—demonstrate that the proposed approach outperformed SOTA WSSS methods, leading to improvement of building extraction from HR RS imager. This article provides a new approach for optimizing pseudomasks generation, and a methodological reference for the applications of weakly supervised on RS images.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • The Outcome of the 2021 IEEE GRSS Data Fusion Contest—Track MSD:
           Multitemporal Semantic Change Detection

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      Authors: Zhuohong Li;Fangxiao Lu;Hongyan Zhang;Lilin Tu;Jiayi Li;Xin Huang;Caleb Robinson;Nikolay Malkin;Nebojsa Jojic;Pedram Ghamisi;Ronny Hänsch;Naoto Yokoya;
      Pages: 1643 - 1655
      Abstract: We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: “Detection of settlements without electricity” and “Multitemporal semantic change detection.” This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Adaptation of the CCSDS 123.0-B-2 Standard for RGB Video Compression

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      Authors: Yubal Barrios;Raúl Guerra;Sebastián López;Roberto Sarmiento;
      Pages: 1656 - 1669
      Abstract: The integration of video sensors on-board satellites is becoming a trend in the space industry, since they provide extra information in the temporal domain when compared with traditional remote sensing imaging acquisition equipment. The inclusion of the temporal dimension together with the constant increase in the sensor resolution supposes a challenge for on-board processing, taking into account the limited computational and storage resources on-board satellites and that it is unfeasible to directly transmit raw video to ground, due to downlink bandwidth limitations. For these reasons, on-board video compression is needed. However, the inherent complexity of the video encoders used on ground limits their implementation on environments with high constraints in terms of computational burden, area, and power consumption. This article proposes an extended compression chain that implements as compression core the CCSDS 123.0-B-2 standard, originally developed for near-lossless compression of multi- and hyperspectral images. In addition, some preprocessing stages are included to manage the temporal dimension of RGB videos efficiently. The proposed solution guarantees low complexity and flexibility to compress both multi- and hyperspectral images and panchromatic and RGB videos by using a single compression instance, which is adapted by adding or removing the appropriate stages. Results demonstrate the viability of this solution to be implemented on space payloads, since high compression ratios are achieved without incurring in a penalty in terms of video quality.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Nationwide Radar-Based Precipitation Nowcasting—A Localization Filtering
           Approach and its Application for Germany

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      Authors: Ricardo Reinoso-Rondinel;Martin Rempel;Markus Schultze;Silke Trömel;
      Pages: 1670 - 1691
      Abstract: One approach to improve advection methods is the short-term ensemble prediction system (STEPS). STEPS decomposes precipitation fields into different spatial scales and filters those having a short lifetime. The latter is achieved by using an auto-regressive (AR) model that considers a sequence of observations. However, such a model tends to smooth nowcasting fields especially in small but convective precipitation areas and at longer lead-times. With focus on the deterministic configuration of STEPS, i.e., the spectral prognosis model (SPROG), this article 1) extends the STEPS approach by estimating spatially localized parameters of the AR process, 2) conducts a sensitivity analysis of the SPROG model to the order of the AR process, the spatial decomposition levels, and post-processing, and 3) analyzes the forecast skill of the extended STEPS. For such purpose, the performance of the localized AR model was demonstrated and evaluated at several precipitation thresholds and window sizes using a varied set of precipitation events collected by the radar network of the German Weather Service. The statistical results exhibited an improved performance of the localized AR model over SPROG when both are evaluated at precipitation thresholds and window sizes larger than 0.1 mm h$^{-1}$ and 1 km, respectively, and for lead-times up to 2 h. The analysis suggested a first-order AR process, six cascade levels, and a mean adjustment post-processing procedure. Our results show a key role of the localization aspect when generating nationwide forecasts in scenarios that include large precipitation areas which are non-uniformly distributed having isolated convective features.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Saline-Sodic Soil EC Retrieval Based on Box-Cox Transformation and Machine
           Learning

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      Authors: Xiaojie Li;Yishan Sun;Xiaopeng Chen;Yingye Li;Tao Jiang;Zhengwei Liang;
      Pages: 1692 - 1700
      Abstract: Electrical conductivity (EC) is an important index of soil salinity level and an essential factor in judging whether improvement can be made and assessing the improvement, if any. However, saline soils of different types show different spectral characteristic bands, which affects the retrieval precision to a certain extent. To tackle this issue, we present a novel way of modeling. Specifically, we compare the correlation of sensitive bands, multiplication of sensitive bands, and spectral indexes with EC data to determine the optimal input parameters, and take the EC data after Box-Cox transformation as the output. We then try out several machine-learning algorithms to establish retrieval models for soil EC. Validation results show that the model precision increases by 3.23–85.71% after EC data transformation and machine learning models produce 29.41–86.67% higher precision than the linear regression model. Finally, using the optimal model, we retrieve pixel-level (10 m × 10 m) EC for the soil in our study areas. This provides necessary data support for working out soil desalinization initiatives and evaluating the effectiveness of such initiatives.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Improving Fractional Vegetation Cover Estimation With Shadow Effects Using
           High Dynamic Range Images

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      Authors: Wei Chen;Zhe Wang;Xuepeng Zhang;Guangchao Li;Fengjiao Zhang;Lan Yang;Haijing Tian;Gongqi Zhou;
      Pages: 1701 - 1711
      Abstract: Measured fractional vegetation cover (FVC) on the ground is very important for validation of the remote sensing products and algorithms. However, because of the influence of some factors such as the angle of illumination and vegetation density, the existence of vegetation shadows limits the accuracy of FVC estimation. This article proposes a deep learning method to reduce the FVC estimation error based on high dynamic range (HDR) images with vegetation shadows (HDR REC-DL method). The HDR REC-DL method can accurately extract FVC from HDR images with complex texture information on vegetation shadows. This method is based on the U-Net convolutional network structure for semantic segmentation of images containing vegetation shadows, and the segmentation results are less affected by vegetation types. Results from the HDR REC-DL method were highly similar to the vegetation segmentation results from visual interpretation. Values of the kappa coefficient, F1 score (F1), recall, and mean intersection over union of the HDR REC-DL method were 0.926, 0.942, 0.924, 0.916 for sunny weather and 0.903, 0.974, 0.983, and 0.895 for cloudy weather, respectively. Compared with the vegetation segmentation accuracy of the shadow-resistant algorithm, the HDR REC-DL method increases the kappa coefficient, F1, and mIOU by 21%, 16%, and 29% for sunny weather, and by 11.1%, 3.6%, and 10.3% for cloudy weather, respectively. The HDR REC-DL method provides a novel method for accurately estimating FVC from images containing vegetation shadows.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • How to Get the Most Out of U-Net for Glacier Calving Front Segmentation

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      Authors: Maniraman Periyasamy;Amirabbas Davari;Thorsten Seehaus;Matthias Braun;Andreas Maier;Vincent Christlein;
      Pages: 1712 - 1723
      Abstract: The melting of ice sheets and glaciers is one of the main contributors to global sea-level rise. Hence, continuous monitoring of glacier changes and in particular the mapping of positional changes of their calving front is of significant importance. This delineation process, in general, has been carried out manually, which is time-consuming and not feasible for the abundance of available data within the past decade. Automatic delineation of the glacier fronts in synthetic aperture radar (SAR) images can be performed using deep learning-based U-Net models. This article aims to study and survey the components of a U-Net model and optimize the model to get the most out of U-Net for glacier (calving front) segmentation. We trained the U-Net to segment the SAR images of Sjogren-Inlet and Dinsmoore–Bombardier–Edgworth glacier systems on the Antarctica Peninsula region taken by ERS-1/2, Envisat, RadarSAT-1, ALOS, TerraSAR-X, and TanDEM-X missions. The U-Net model was optimized in six aspects. The first two aspects, namely data preprocessing and data augmentation, enhanced the representation of information in the image. The remaining four aspects optimized the feature extraction of U-Net by finding the best-suited loss function, bottleneck, normalization technique, and dropouts for the glacier segmentation task. The optimized U-Net model achieves a dice coefficient score of 0.9378 with a 20% improvement over the baseline U-Net model, which achieved a score of 0.7377. This segmentation result is further postprocessed to delineate the calving front. The optimized U-Net model shows 23% improvement in the glacier front delineation compared to the baseline model.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Novel Approach to Map the Intensity of Surface Melting on the Antarctica
           Ice Sheet Using SMAP L-Band Microwave Radiometry

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      Authors: Mohammad Mousavi;Andreas Colliander;Julie Z. Miller;John S. Kimball;
      Pages: 1724 - 1743
      Abstract: The polar ice sheets have undergone unprecedented melt events in recent years, which have consequences for the ice sheet mass balance and stability, and global sea level. In this article, we employed L-band (1.4 GHz) brightness temperature observations collected by NASA's Soil Moisture Active Passive (SMAP) mission to investigate the extent, duration, and intensity of melt events on the Antarctic Ice Sheet from 2015 to 2020. The observed microwave response depends on the sensor measurement frequency. Our hypothesis for this article is that the relatively long wavelength (21 cm) SMAP observations can detect a wider range of surface wetness conditions relative to shorter wavelength microwave observations that attain signal saturation at relatively lower wetness levels and within shallower surface layers. SMAP provides nearly all-weather surface monitoring over all of Antarctica twice daily with morning and evening overpasses at about 40 km spatial resolution. We applied an empirical threshold algorithm using horizontally and vertically polarized microwave brightness temperature differences to detect surface melt events over Antarctica. The results show that the SMAP empirical algorithm can be used to detect melt extent and duration, and the geophysical model-based algorithm can be used to detect variations in snow wetness, which serve as an indicator of melt intensity. Analysis of the melt seasons between 2015 and 2020 shows that even though the melt extent in 2019–2020 was not as large as during the 2015–2016 melt season, it was significantly more intense, particularity on the West Antarctic Ice Sheet.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • SADA-Net: A Shape Feature Optimization and Multiscale Context
           Information-Based Water Body Extraction Method for High-Resolution Remote
           Sensing Images

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      Authors: Bin Wang;Zhanlong Chen;Liang Wu;Xiaohong Yang;Yuan Zhou;
      Pages: 1744 - 1759
      Abstract: Convolutional neural networks (CNNs) have significance in remote sensing image mapping, and pixel-level representation allows refined results. Due to inconsistencies within a class and different scales of water bodies, the water body mapping has challenges, such as insufficient integrity and rough shape segmentation. To resolve these issues, we proposed an intelligent water bodies extraction method (named SADA-Net) for high-resolution remote sensing images. This method considers multiscale information, context dependence, and shape features. The network framework integrates three critical components: shape feature optimization (SFO), atrous spatial pyramid pooling, and dual attention modules. SADA-Net can accurately extract an extensive range of water bodies in complex scenarios. SADA-Net has certain advantages regarding small and dense water bodies extraction, as the SFO module effectively solves the defects of the unified processing of low-level features in the encoder stage of CNNs, which highlights the shape information of a water body. Two data types (red, green, and blue bands and multispectral images) are employed to verify the performance of the proposed network. The best result achieved an evaluation index F1-Score of 96.14% in large-scale image segmentation, and the structural similarity index measure reached 94.70%. Overall, the proposed method achieves the purpose of maximizing the integrity and optimizing the shape of a water body. Additionally, the SADA-Net proposed in this article has a specific reference value for high-resolution remote sensing image water bodies mapping.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Real-Time Variants of Vertical Synchrosqueezing: Application to Radar
           Remote Sensing

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      Authors: Karol Abratkiewicz;Jacek Gambrych;
      Pages: 1760 - 1774
      Abstract: This article presentsthorough research on using high-order vertical synchrosqueezing (VSS) in different radar remote sensing applications. The method well established in the literature is examined and compared to the novel form of third-order VSS and first-order VSS using an enhanced estimator of the instantaneous frequency, both proposed by the authors. An investigation shows that the two introduced variants of VSS are characterized by preserved capabilities (understood as the possibility to concentrate the time-frequency distribution and its reconstruction) with significantly reduced computation cost. The research shows that in practical radar remote sensing applications, high-order VSS can be successfully replaced by the approach proposed in this article with a lower computational burden. Furthermore, the methods are validated under numerical experiments, both simulated and real-life, which showed the efficiency of the proposed methods in radar signal processing, particular component extraction, and signal decomposition. Moreover, the authors developed the real-time graphical-processing-unit-based implementation of the proposed techniques and presented its efficiency in practical conditions.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Reweighted Nuclear Norm and Total Variation Regularization With Sparse
           Dictionary Construction for Hyperspectral Anomaly Detection

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      Authors: Xiaoyi Wang;Liguo Wang;Jiawen Wang;Kaipeng Sun;Qunming Wang;
      Pages: 1775 - 1790
      Abstract: Hyperspectral anomaly detection is an important technique in the field of remote sensing image processing. Over the last few years, low rank and sparse matrix decomposition (LRSMD) has played an increasingly significant role in hyperspectral anomaly detection. The detection performance of the LRSMD-based anomaly detectors is primarily determined by prior constraints and the background dictionary construction method. To increase the detection accuracy, we proposed the reWeighted Nuclear Norm and total variation regularization with Sparse Dictionary construction for hyperspectral Anomaly Detection (WNNSDAD), which incorporated reweighted nuclear norm and total variation regularizations as the prior constraints into the LRSMD model, and constructed a sparse background dictionary without the need of clustering. Compared to the standard nuclear norm, the reweighted nuclear norm helped to overcome the challenge of an unbalanced penalty for a singular value and ensure a more effective low rank approximation. Simultaneously, total variation regularization was introduced as a piecewise smoothing constraint, which helped to maintain the spatial correlation of the hyperspectral image. Additionally, we proposed a background dictionary construction method, by which a relatively complete background dictionary could be obtained without clustering, and the background part could be represented more reliably. The experiments on seven real-world hyperspectral datasets show that in comparison to eight state-of-the-art anomaly detection methods, the proposed WNNSDAD method demonstrated greater accuracy.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Mapping Gridded Gross Domestic Product Distribution of China Using Deep
           Learning With Multiple Geospatial Big Data

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      Authors: Yuehong Chen;Guohao Wu;Yong Ge;Zekun Xu;
      Pages: 1791 - 1802
      Abstract: Timely griddedgross domestic product (GDP) data is a fundamental indicator in many applications. It is critical to characterize the complex relationship between GDP and its auxiliary information for accurately estimating gridded GDP. However, few knowledge is available about the performance of deep learning approaches for learning this complex relationship. This article develops a novel convolutional neural network based GDP downscaling approach (GDPnet) to transform the statistical GDP data into GDP grids by integrating various geospatial big data. An existing autoencoder-based downscaling approach (Resautonet) is employed to compare with GDPnet. The latest county-level GDP data of China and the multiple geospatial big data are adopted to generate the 1-km gridded GDP data in 2019. Due to the different related auxiliary data of each GDP sector, the two downscaling approaches are first separately built for each GDP sector and then the results are merged to the gridded total GDP data. Experimental results show that the two deep learning approaches had good predictive power with R2 over 0.8, 0.9, and 0.92 for the three sectors tested by county-level GDP data. Meanwhile, the proposed GDPnet outperformed the existing Resautonet. The average R2 of GDPnet was 0.034 higher than that of Resautonet in terms of county-level GDP test data. Furthermore, GDPnet had higher accuracy (R2 = 0.739) than Resautonet (R2 = 0.704) assessed by town-level GDP data. In addition, the proposed GDPnet is faster (about 78% running time) than the Resautonet. Hence, the proposed approach provides a valuable option for generating gridded GDP data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Reconstruction of Satellite Time Series With a Dynamic Smoother

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      Authors: Jordan Graesser;Radost Stanimirova;Mark A. Friedl;
      Pages: 1803 - 1813
      Abstract: Time series reconstruction methods are widely used to generate smooth and gap-free time series using imagery acquired at coarse spatial resolution and high frequency return intervals. However, as interest has grown in leveraging the nearly 40-a record of Landsat to study long-term changes in terrestrial ecosystems at 30-m spatial resolution, new methods are required to reconstruct time series of Landsat imagery, which have lower temporal density than coarse resolution sensors such as AVHRR or MODIS. To address this need, we introduce a dynamic temporal smoothing (DTS) method that reconstructs sparse and noisy signals into dense time series at regular intervals. The DTS is a weighted smoother with parameters that adjust dynamically to variation in time series and can be applied to both dense and sparse time series measurements. Because the DTS smoother we describe is specifically designed to reconstruct high-quality time series of optical imagery, it has utility for applications focused on land cover and vegetation remote sensing over long time periods at moderate spatial resolution. We present the DTS algorithm that we have implemented and illustrate the ability of the DTS to reconstruct time series of Landsat imagery across multiple sensors (TM, ETM+, and OLI). To demonstrate the effectiveness of the DTS algorithm we apply it and evaluate results across a diverse range of land cover and vegetation types in the South American Southern Cone region.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Progress and Challenges in Intelligent Remote Sensing Satellite Systems

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      Authors: Bing Zhang;Yuanfeng Wu;Boya Zhao;Jocelyn Chanussot;Danfeng Hong;Jing Yao;Lianru Gao;
      Pages: 1814 - 1822
      Abstract: Due to advances in remote sensing satellite imaging and image processing technologies and their wide applications, intelligent remote sensing satellites are facing an opportunity for rapid development. The key technologies, standards, and laws of intelligent remote sensing satellites are also experiencing a series of new challenges. Novel concepts and key technologies in the intelligent hyperspectral remote sensing satellite system have been proposed since 2011. The aim of these intelligent remote sensing satellites is to provide real-time, accurate, and personalized remote sensing information services. This article reviews the current developments in new-generation intelligent remote sensing satellite systems, with a focus on intelligent remote sensing satellite platforms, imaging payloads, onboard processing systems, and other key technological chains. The technological breakthroughs and current defects of intelligence-oriented designs are also analyzed. Intelligent remote sensing satellites collect personalized remote sensing data and information, with real-time data features and information interaction between remote sensing satellites or between satellites and the ground. Such developments will expand the use of remote sensing applications beyond government departments and industrial users to a massive number of individual users. However, this extension faces challenges regarding privacy protection, societal values, and laws regarding the sharing and distribution of data and information.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Change Detection From Synthetic Aperture Radar Images via Graph-Based
           Knowledge Supplement Network

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      Authors: Junjie Wang;Feng Gao;Junyu Dong;Shan Zhang;Qian Du;
      Pages: 1823 - 1836
      Abstract: Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudolabeled samples to guide subsequent training and testing. However, deep networks commonly require many high-quality samples for parameter optimization. The noise in pseudolabels inevitably affects the final change detection performance. To solve the problem, we propose a graph-based knowledge supplement network (GKSNet). To be more specific, we extract discriminative information from the existing labeled dataset as additional knowledge, to suppress the adverse effects of noisy samples to some extent. Afterward, we design a graph transfer module to distill contextual information attentively from the labeled dataset to the target dataset, which bridges feature correlation between datasets. To validate the proposed method, we conducted extensive experiments on four SAR datasets, which demonstrated the superiority of the proposed GKSNet as compared to several state-of-the-art baselines. Our codes are available: at https://github.com/summitgao/SAR_CD_GKSNet.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Evaluating the Ability of NOAA-20 Monthly Composite Data for Socioeconomic
           Indicators Estimation and Urban Area Extraction

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      Authors: Yangguang Li;Zhichao Song;Bin Wu;Bailang Yu;Qiusheng Wu;Yuchen Hong;Shaoyang Liu;Jianping Wu;
      Pages: 1837 - 1845
      Abstract: The new visible infrared imaging radiometer suite (VIIRS) onboard the National Oceanic and Atmospheric Administration-20 satellite (NOAA-20) is capable of imaging the Earth during both daytime and nighttime. The NOAA-20 VIIRS’ day/night band (DNB) enables a new generation of nighttime imaging applications. However, few studies investigated the ability of NOAA-20 DNB nighttime light data in modeling socioeconomic indicators [such as the gross domestic product (GDP) and electric power consumption (EPC)] and extracting urban areas. In this article, we first used a simple linear regression model to investigate the potential of NOAA-20 nighttime light data for estimating GDP and EPC at multiple scales; we then extracted urban areas from NOAA-20 nighttime light data by using support vector machines. Taking mainland China as the study area, we found that the correlation coefficient of determination R2 between NOAA-20 nighttime light data and GDP/EPC at both provincial and prefectural scales are higher than 0.7. By comparing the results with NPP-VIIRS nighttime light data, similar R2 values were obtained at both two scales, indicating that NOAA-20 nighttime light data and NPP-VIIRS data are comparable in estimating socioeconomic indicators. Moreover, NOAA-20 also shows a similar detection ability with NPP-VIIRS in extracting urban areas. This article demonstrated that NOAA-20 and NPP-VIIRS are comparable in economic statistics estimation and urban area extraction. The NOAA-20 nighttime light data can be a useful data source for enlightening more applications in the fields of socioeconomic and urban studies.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Combining a Crop Growth Model With CNN for Underground Natural Gas Leakage
           Detection Using Hyperspectral Imagery

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      Authors: Ying Du;Jinbao Jiang;Ziwei Liu;Yingyang Pan;
      Pages: 1846 - 1856
      Abstract: Natural gas leakage occurs frequently due to aging pipes and other factors, but is challenging to detect. In this article, a new, robust method for nondestructive natural gas microleakage detection was proposed. It combines a crop growth model with a convolutional neural network (CNN) approach to quantitatively detect underground natural gas leakage using unmanned aerial vehicle (UAV) hyperspectral imagery. The environmental stress on wheat was used as an indicator to reflect the intensity of natural gas leakage. First, a crop growth model (simple algorithm for yield, SAFY) was used to simulate the growth of wheat, and the environmental stress factor in the model was used to construct the natural gas stress index (Kgs). Subsequently, CNN models were used to estimate the Kgs value with a hyperspectral image as the input. Finally, the CNN estimated Kgs was used to detect the natural gas leakage in the study area. Results showed that the SAFY model Kgs value could effectively identify natural gas leakage, with statistically significant differences (p-value < 0.05) among three leakage levels. Furthermore, compared to a single spectral index, Kgs had superior robustness throughout the wheat growth period. The CNN-1D model with InceptionV2 architecture exhibited the best accuracy in estimating Kgs, with a robust nRMSE of 6.9%. Overall, the combined CNN and SAFY models could accurately detect natural gas leakage, and this approach is more robust than traditional spectral index-based methods. This article provides a new method for nondestructive detecting of natural gas microleakage.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Improvement of MiRS Sea Surface Temperature Retrievals Using a Machine
           Learning Approach

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      Authors: Shuyan Liu;Christopher Grassotti;Quanhua Liu;Yan Zhou;Yong-Keun Lee;
      Pages: 1857 - 1868
      Abstract: We report on the development of a machine learning approach to improving sea surface temperature (SST) retrievals based on satellite-based microwave channel measurements at frequencies higher than 23 GHz. The approach uses a deep neural network (DNN) trained using Microwave Integrated Retrieval System physical retrievals as inputs and collocated European Centre for Medium-Range Weather Forecasts analyses for training and validation. The DNN was designed to characterize SST retrieval residual and then used to correct the original retrieval. Evaluation based on one year of independent data showed reduction in retrieval residual standard deviation from 3.22 to 1.80 K in January and 3.02 to 1.92 K in July and reduction in mean residual from 0.30 to 0.08 K in January and 0.61 to 0.22 K in July. Comparisons with multilinear regression and machine learning approaches that used measured brightness temperatures as inputs were significantly less effective in retrieving SST directly, although the DNN used brightness temperature also showed improvements. This indicates that physical retrieval provides valuable information useful in characterizing retrieval residual beyond that of the measured radiances. The DNN approach also effectively removed scan angle dependence of retrieval residuals—an important consideration with cross-track instruments. Sensitivity tests indicated that skill declines with time as time increases from training month, but that skill in the same month, one year later is nearly the same as that of the original training month. This suggests that it may be sufficient to pretrain a stratified model with monthly or seasonal dependence using one full annual cycle, which could then be used in subsequent years with continued good performance.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Methodology of Detection and Classification of Selected Aviation Obstacles
           Based on UAV Dense Image Matching

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      Authors: Marta Lalak;Damian Wierzbicki;
      Pages: 1869 - 1883
      Abstract: Currently, more and more accurate data provided by UAVs make it possible to analyze land cover, which requires the detection of objects and their individual elements. Object detection and determination of their geometric features is possible thanks to dense point clouds generated based on imagery obtained from low altitudes. 3D data from UAVs turn out to be extremely useful for ensuring safety in the airspace in the close vicinity of the airport. This article presents the methodology of automatic aviation obstacle detection based on low altitude data (UAV). The research was carried out on a dense 3D point cloud. The developed methodology for detecting aviation obstacles consists of three main stages. The first is point cloud filtration based on height–preliminary identification of aviation obstacles, followed by 3D point cloud segmentation using a modified RANSAC algorithm, supplemented with two-dimensional vector data of aviation obstacles to improve the accuracy of the segmentation process. The last stage is the classification of aviation obstacles according to the adopted height and cross-section criterion. The proposed method of detecting aviation obstacles is characterized by high accuracy. The mean error of fitting the point cloud to the obstacle database ranged from ± 0.04 m to ± 0.07 m.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Transformer-Driven Semantic Relation Inference for Multilabel
           Classification of High-Resolution Remote Sensing Images

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      Authors: Xiaowei Tan;Zhifeng Xiao;Jianjun Zhu;Qiao Wan;Kai Wang;Deren Li;
      Pages: 1884 - 1901
      Abstract: It is hard to use a single label to describe an image for the complexity of remote sensing scenes. Thus, it is a more general and practical choice to use multilabel image classification for high-resolution remote sensing (HRS) images. How to construct the relation between categories is a vital problem for multilabel classification. Some researchers use the recurrent neural network (RNN) or long short-term memory (LSTM) to exploit label relations over the last years. However, the RNN or LSTM could model such category dependence in a chain propagation manner. The performance of the RNN/LSTM might be questioned when a specific category is improperly inferred. To address this, we propose a novel HRS image multilabel classification network, transformer-driven semantic relation inference network. The network comprises two modules: semantic sensitive module (SSM) and semantic relation-building module (SRBM). The SSM locates the semantic attentional regions in the features extracted by a deep convolutional neural network and generates a discriminative content-aware category representation (CACR). The SRBM uses label relation inference from outputs of the SSM to predict final results. The characteristic of the proposed method is that it can extract semantic attentional regions relevant to the category and generate a discriminative CACR and natural and interpretable reasoning about label relations. Experiments were performed on the public UCM multilabel and MLRSNet datasets. Quantitative and qualitative analyses on state-of-the-art multilabel benchmarks proved that the proposed method could effectively locate semantic regions and build relationships between categories with better robustness.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Incorporating the Completeness and Difficulty of Proposals Into Weakly
           Supervised Object Detection in Remote Sensing Images

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      Authors: Xiaoliang Qian;Yu Huo;Gong Cheng;Xiwen Yao;Ke Li;Hangli Ren;Wei Wang;
      Pages: 1902 - 1911
      Abstract: Weakly supervised object detection (WSOD) in remote sensing images (RSI) only require image-level labels to detect various objects. Most of the WSOD methods incline to capture the most discriminative parts of object rather than the entire object, and the number of easy and hard samples is imbalanced. To address the first problem, a novel metric named objectness score (OS) is proposed and incorporated into the training loss of our WSOD model. The OS is consisted of the traditional class confidence score (CCS) and the object completeness prior score (OCPS). The CCS can provide the probability that a proposal belongs to a certain class, and the OCPS can quantify the completeness that a proposal covers the entire object. Therefore, the samples which cover the entire object with high class confidences will be assigned large weight in the training loss through OS. To handle the second problem, a novel metric named difficulty evaluation score (DES) is proposed and also incorporated into the training loss. The DES is calculated by using the entropy of confidence score vector of each proposal and is used to quantify how difficult a proposal can be identified correctly, consequently, the hard samples will also be assigned large weight in the training loss through DES. The ablation experiments on two RSI datasets verify the effectiveness of the proposed OS and DES. The comprehensive quantitative and subjective evaluations demonstrate that our method inclines to detect the entire object accurately, and surpasses seven state-of-the-art WSOD methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Tree Roots Reconstruction Framework for Accurate Positioning in
           Heterogeneous Soil

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      Authors: Wenhao Luo;Yee Hui Lee;Hai-Han Sun;Lai Fern Ow;Mohamed Lokman Mohd Yusof;Abdulkadir C. Yucel;
      Pages: 1912 - 1925
      Abstract: Ground-penetrating radar has recently found wide application in the underground imaging of tree roots. However, ignoring the random and complex nature of the heterogeneous soil and assuming the soil's relative permittivity constant throughout the survey region may yield an inaccurate tree root positioning. Meanwhile, the incompatible relative soil permittivity results in low image quality of the roots reconstruction. Furthermore, the soil's spatial heterogeneity introduces unwanted environmental clutter in the mapping of the tree root. A data processing framework is proposed to address these issues for retrieving the tree roots in heterogeneous soil environments. The proposed framework combines four techniques to be applied consecutively: First, a hyperbola extraction method based on a column-connection clustering algorithm is used to extract individual hyperbolae in B-scans, eliminate mutual influence in the process, and suppress noise. Second, an improved Hough transform technique is adopted to estimate the equivalent permittivity of each root's surrounding soil environment for each extracted hyperbola. Third, individual root restoration is done by transferring each hyperbola to a spot using its corresponding soil equivalent permittivity. Finally, individually restored features are combined in the final image. The images obtained via the proposed framework show a well reconstructed two-dimensional tree roots scenario. The applicability and the effectiveness of the proposed framework have been demonstrated through numerical simulations and fieldmeasurements.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multilayer Cascade Screening Strategy for Semi-Supervised Change Detection
           in Hyperspectral Images

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      Authors: Lian Liu;Danfeng Hong;Li Ni;Lianru Gao;
      Pages: 1926 - 1940
      Abstract: Change detection (CD) is an important application of remote sensing, which provides information about land cover changes on the Earth's surface. Hyperspectral image (HSI) can show more spectral information, which greatly improves the ability of remote sensing to identify change features. The challenge is how to overcome the scarcity of labeled samples and extract the change information of high-dimensional spectra in HSI. To solve the previous problem, a semi-supervised CD with multilayer cascade screening strategy (MCS4CD) that uses both the spatial information and active learning is proposed to select highly reliable unlabeled samples to increase the training sets. The MCS4CD method can effectively use unlabeled samples to improve accuracy. Additionally, a subspace CD method based on iterative slow feature analysis is designed to extract the most temporally invariant component from the high-dimensional space. Experimental results on four hyperspectral datasets show that with a small number of labeled samples, the proposed method achieves a much better performance than existing CD methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Small Target Detection From Infrared Remote Sensing Images Using Local
           Adaptive Thresholding

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      Authors: Chang Liu;Fengying Xie;Xiaomeng Dong;Hongxia Gao;Haopeng Zhang;
      Pages: 1941 - 1952
      Abstract: Small target detection from the infrared remote sensing image is a challenge task. In this article, a novel local adaptive threshold algorithm combined with heterogeneity and compactness filters is proposed to detect the small target from the infrared remote sensing images. First, the infrared image is filtered by a heterogeneity filter to enhance the target saliency. Then, the enhanced image is filtered by a compactness filter to generate a target candidate region map. Finally, for each pixel in the target candidate region, a local adaptive threshold is calculated from the enhanced image to determine whether it is a target pixel or not, and thus, the targets are extracted out. The designed heterogeneity filter and compactness filter can effectively suppress the background clutter, enhance the target, and generate target candidate regions. The proposed adaptive thresholding is a local threshold method, which is calculated in a small local window and can effectively reduce the false alarm and missing alarm. Qualitative and quantitative experiments are conducted on synthetic images and real images. The experiment results show that, with good target enhancement and background suppression, and high detection accuracy, the proposed method outperforms other state-of-the-art methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Land-Cover Classification With Time-Series Remote Sensing Images by
           Complete Extraction of Multiscale Timing Dependence

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      Authors: Jining Yan;Jingwei Liu;Lizhe Wang;Dong Liang;Qingcheng Cao;Wanfeng Zhang;Jianyi Peng;
      Pages: 1953 - 1967
      Abstract: The evolution rules of different land covers show some similarity, which poses a huge challenge for high-accuracy land-cover classification. Therefore, the full extraction of multiscale timing-dependence features is important for mining seasonal and phenological change laws and improving the accuracy of time-series land-cover classification. However, traditional methods are often unable to fully detect the global and local change information generated during the evolution of land covers, resulting in incomplete timing-dependence features being extracted and a low classification accuracy. The Informer network can fully capture the long-term dependence of a time series, thereby improving its classification accuracy. Therefore, we propose a high-accuracy land-cover classification method with the Informer network. First, we continuously shorten the length of the series so that the ProbSparse self-attention mechanism can consider timing dependencies on multiscale, and then we can obtain the features of the local important moments. Second, we calculate the correlation between the important moments and the other moments, as well as the correlation among each moment, to fully utilize the local and global time-dependent features of the land-cover time series. Third, we add a fully connected batch normalization module in order to use all the extracted timing dependence for classification. Finally, the proposed model is compared with traditional models on two datasets: for the reorganized BreizhCrops dataset, it achieved a performance similar to long short-term memory; for the TiSeLaC dataset, it achieved an F1-score of 96.011%, which is 0.33% higher than the second-best model.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Quantifying the Representativeness Errors Caused by Scale Transformation
           of Remote Sensing Data in Stochastic Ensemble Data Assimilation

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      Authors: Feng Liu;Zebin Zhao;Xin Li;
      Pages: 1968 - 1980
      Abstract: Representativeness error caused by scale transformation (REST) is an intrinsic property of data assimilation, as assimilating new observations likely involves the fusion of multisource and multiscale data. Earlier studies focused on specific cases and failed to obtain a general concept. This study attempts to achieve a further understanding of REST in both theory and practice. Based on scale-related definitions and formulations, the statistical RESTs of observation errors and analysis errors are deduced in stochastic ensemble data assimilation. Experiments based on ensemble Kalman filter are conducted to validate the interpretability of the proposed formulations. A synthetic experiment uses the stochastic Lorenz model as the forecasting operator, and a real-world experiment employs a simple biosphere model as the forecasting operator and uses a series of mixed ground-based and remote sensing soil moisture observations. The results confirm that REST should be proportional to the scale difference when assimilating direct observations and both system states and observations are homogeneous processes. Due to the nonlinearity in modeling, assimilation, and scale transformation, increasing RESTs are found if the scale of the observation is much larger than that of the state space, or multiscale observations are added into the assimilation system. Quantifying REST improves the understanding of uncertainty in data assimilation, but further studies on REST are required in both theory and practice, for example, REST correlates with other errors in forcing, parameters, and models, and introduces an observation operator to assimilate indirect observations.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • New Ku-Band Geophysical Model Function and Wind Speed Retrieval Algorithm
           Developed for Tiangong-2 Interferometric Imaging Radar Altimeter

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      Authors: Guo Li;Yunhua Zhang;Xiao Dong;
      Pages: 1981 - 1991
      Abstract: A Ku-band geophysical model function for wind speed retrieval named as KuLMOD-H is proposed based on the quasi-specular reflection model for the Chinese Tiangong-2 Interferometric Imaging Radar Altimeter (TG2-InIRA), which can be used to retrieve the wind speed with 2 km resolution. The model is derived by expanding the effective nadir reflection coefficient term and the mean square slope term in the quasi-specular reflection model using quadratic polynomials with wind speed as variable. The model coefficients are obtained by fitting the radar backscattering coefficient data from TG2-InIRA to the collocated sea surface wind speed data from European Center for Medium-Range Weather Forecasts (ECMWF). For solving the problem of potential ambiguous solutions when the incidence angles are relatively large, a regularization approach is further proposed. The retrieved wind speed results have a root-mean-square error (RMSE) of 1.42 m/s compared with the collocated ECMWF wind speed data and at the same time, they are highly consistent with the buoy data. Different from the previous works on TG2-InIRA wind speed retrieval, this article derives a semiphysical model suitable for incidence angles from 1$^circ$ to 8$^circ$, by which better retrieval results are achieved. This work not only explores the wind speed retrieval capability of the instrument under low incidence-angle observation, but also provides high-resolution wind speed data for further correction of sea state bias for TG2-InIRA sea surface height measurements.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • High-Resolution Real-Time Imaging Processing for Spaceborne Spotlight SAR
           With Curved Orbit via Subaperture Coherent Superposition in Image Domain

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      Authors: Yanbin Liu;Guang-Cai Sun;Liang Guo;Mengdao Xing;Hanwen Yu;Ruimin Fang;Shiyu Wang;
      Pages: 1992 - 2003
      Abstract: With the increasing of application requirements, the high-resolution real-time imaging processing of the spaceborne spotlight synthetic aperture radar (SAR) has been developed. Since the traditional real-time imaging algorithms have the problems that the range model has errors and the two-dimensional (2-D) space-variance of the equivalent velocity caused by the curved orbit cannot be effectively eliminated. Thus, this article proposes a high-resolution real-time imaging algorithm for spaceborne spotlight SAR with curved orbit via subaperture coherent superposition in image domain. In this article, the echo data are first divided into subapertures to avoid the azimuth spectrum aliasing. After that, the 2-D space-variance of the equivalent velocity caused by the curved orbit can be eliminated by the method of azimuth time scale transformation, higher order phase compensation, and introducing phase transition function. Then, the dechirp function is applied for the subaperture signals to obtain the partial-resolution subaperture images. Finally, these partial-resolution subaperture images are coherently superposed in the image domain to obtain the final full-resolution image of the whole echo data. Moreover, the proposed algorithm improves the real-time performance by adopting the idea that the subaperture data recording and subaperture real-time imaging processing are synchronized, which greatly accelerates the acquisition of the final full-resolution imaging result. At the end of this article, the simulations and the real-time performance analysis are performed to validate the proposed algorithm.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Deriving Surface Reflectance From Visible/Near Infrared and Ultraviolet
           Satellite Observations Through the Community Radiative Transfer Model

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      Authors: Quanhua Liu;Banghua Yan;Kevin Garrett;Yingtao Ma;Xingming Liang;Jingfeng Huang;Wenhui Wang;Changyong Cao;
      Pages: 2004 - 2011
      Abstract: Earth's surface reflectance is an important parameter affecting ultraviolet (UV) and visible (VIS) radiance calculations at the top of the atmosphere because many UV and VIS channels can acquire information about the surface and atmosphere. This article provides the theoretical basis for deriving the surface reflectance from satellite-measured UV and VIS observations at window and lower sounding channels with the help of the community radiative transfer model (CRTM) and collocated atmospheric profiles such as ozone, water vapor, and aerosols. Cirrus cloud may be included in the calculation as long as the observations contain enough reflected radiation from the surface. An explicit equation with three scalar parameters $alpha $, $beta $, and $delta $ is obtained for users to calculate Lambertian surface reflectance from the observation. The expressions for the three parameters are somewhat complicated and computationally expansive. We found a simple and smart way that can exactly calculate the three parameters with quasi-linear functions. Numerical experiments using the CRTM simulations have demonstrated the algorithm accuracy for the surface reflectance retrieval better than 2.0E-14. As a case study, measured surface reflectance and the derived surface reflectance over desert from satellite UV measurements are compared. The derived surface reflectance from Suomi National Polar-orbiting Partnership. Visible Infrared Imaging Radiometer Suite (VIIRS) observations and the VIIRS reflectance product are compared as well. In addition, this methodology can also be used to calculate microwave and infrared surface emissivity with scatterings and solar radiation by adding the surface Planck radiance at the surface temperature.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Exploring the Potential of Spatially Downscaled Solar-Induced Chlorophyll
           Fluorescence to Monitor Drought Effects on Gross Primary Production in
           Winter Wheat

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      Authors: Qiu Shen;Jingyu Lin;Jianhua Yang;Wenhui Zhao;Jianjun Wu;
      Pages: 2012 - 2022
      Abstract: The impacts of drought on the terrestrial gross primary production (GPP) are the most intense and widespread in all extreme climate events. Solar-induced chlorophyll fluorescence (SIF) is considered as a direct representative of actual vegetation photosynthesis and has better performance in monitoring vegetation conditions than greenness-based vegetation indices (VIs) during drought events. Based on the spatially downscaled SIF (SIFds), VIs and GPP products, we explored the potential of SIFds to monitor drought effects on GPP in winter wheat. First, the spatiotemporal dynamics of hydrometeorological factors and vegetation variables in winter wheat during drought events were observed. Then, the SIFds—GPP relationships in different phenological stages were examined in the rainfed area. Finally, the drought-induced GPP losses in different phenological stages were evaluated by scaling SIFds to GPP based on the linear SIFds–GPP relationship in the rainfed area. Results showed that SIFds could capture the spatiotemporal dynamics of drought-induced GPP variations in winter wheat during drought events, and it could quantify accurately the drought-induced GPP losses, with higher sensitivities to GPP changes during the vigorous growing periods. Our study reveals the applicability of SIFds to achieve regional agricultural drought detection and drought-induced GPP loss assessment, which can provide some help for crop adaptation management.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Sentinel-1-Based Water and Flood Mapping: Benchmarking Convolutional
           Neural Networks Against an Operational Rule-Based Processing Chain

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      Authors: Max Helleis;Marc Wieland;Christian Krullikowski;Sandro Martinis;Simon Plank;
      Pages: 2023 - 2036
      Abstract: In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). This comparison is made using a globally distributed dataset of Sentinel-1 scenes and the corresponding ground truth water masks derived from Sentinel-2 data to evaluate the performance of the classifiers on a global scale in various environmental conditions. The impact of using single versus dual-polarized input data on the segmentation capabilities of AlbuNet-34 is evaluated. The weighted cross entropy loss is combined with the Lovász loss and various data augmentation methods are investigated. Furthermore, the concept of atrous spatial pyramid pooling used in DeepLabV3+ and the multiscale feature fusion inherent in U-Net++ are assessed. Finally, the generalization capacity of AlbuNet-34 is tested in a realistic flood mapping scenario by using additional data from two flood events and the Sen1Floods11 dataset. The model trained using dual polarized data outperforms the S-1FS significantly and increases the intersection over union (IoU) score by 5%. Using a weighted combination of the cross entropy and the Lovász loss increases the IoU score by another 2%. Geometric data augmentation degrades the performance while radiometric data augmentation leads to better testing results. FCN/DeepLabV3+/U-Net/U-Net++ perform not significantly different to AlbuNet-34. Models trained on data showing no distinct inundation perform very well in mapping the water e-tent during two flood events, reaching IoU scores of 0.96 and 0.94, respectively, and perform comparatively well on the Sen1Floods11 dataset.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Land Surface Temperature Reconstruction Under Long-Term Cloudy-Sky
           Conditions at 250 m Spatial Resolution: Case Study of Vinschgau/Venosta
           Valley in the European Alps

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      Authors: Paulina Bartkowiak;Mariapina Castelli;Alice Crespi;Georg Niedrist;Damiano Zanotelli;Roberto Colombo;Claudia Notarnicola;
      Pages: 2037 - 2057
      Abstract: In this article, we present a new concept for predicting satellite-derived land surface temperature (LST) under cloudy skies over vegetated areas in the Alps. Although many different reconstruction methods have been developed, they require rarely available inputs, or they restore missing pixels from clear-sky observations with low spatial resolution (1–5 km), which makes them unreliable in heterogenous ecosystems. Given these limitations, we propose a station-based procedure to predict cloud-covered grids from 1-km Terra MODIS LST at 250 m spatial resolution. First, we explored correlations between ground-measured LST and air temperature in conjunction with other geo-biophysical variables under cloudy-sky conditions derived from ESRA clear-sky radiation model. Considering a high site dependency driven by different landcovers, in-situ data were aggregated into three groups (forest, permanent crops, grassland) and then, models were established. Next, the regressions were applied to 250-m gridded predictors to estimate cloud-covered LST pixels for six Terra MODIS LST images in 2014. While for permanent crops and forest group linear modelling was the most efficient, neural networks achieved the best performance for grasslands. The reconstructions showed reasonable LST distribution considering landscape heterogeneity of the region. The results were validated against timeseries of ground-measured LST in 2014. The models achieved reliable performance with an average R2 of 0.84 and root-mean-square error of 2.12 °C. Despite some limitations, mainly due to diversified character of cloudy-sky conditions and high heterogeneity of gridded predictors, the method can effectively reconstruct overcast MODIS data at subpixel level, which shows great potential for producing cloud-free LSTs in complex ecosystems
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An Intercomparison Study of Algorithms for SMAP Brightness Temperature
           Resolution Enhancement With or Without Information From AMSR2

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      Authors: Hanyu Lu;Qinye He;Tianjie Zhao;Panpan Yao;Zhiqing Peng;Tianjian Lu;Haishen Lü;
      Pages: 2058 - 2069
      Abstract: Soil moisture is an essential variable for understanding water and heat exchanges between land and the atmosphere. Presently, L-band remote sensing technology has been widely employed for routine measurement of soil moisture from space. However, the spatial resolution of L-band soil moisture products obtained from microwave radiometers is too low (dozens of kilometres) to meet the needs of practical applications, such as hydrology modeling, weather forecasts, agricultural applications and water resource management. Therefore, this article proposes a new concept to downscale the soil moisture active passive (SMAP) L-band brightness temperature by using advanced microwave scanning radiometer-2 (AMSR2) X-band TB data, including the time-series regression (TSR) algorithm and two-dimensional discrete wavelet transform algorithm. An intercomparison study was conducted over a semiarid area located in the Shandian river basin with the other two algorithms of the Backus–Gilbert (BG) optimal interpolation and natural neighbor interpolation without using the X-band TB data. The results revealed that the BG algorithm outperformed the NNI, 2D-DWT, and TSR algorithms compared with the original 36-km SMAP TB and airborne 1-km TB data. However, the soil moisture retrievals within one 9-km pixel with 8 soil moisture stations showed that the downscaled L-band TB with X-band data are reliable with lower unbiased root-mean-squared errors compared with resolution-enhanced TB without AMSR2 data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Improving Out-of-Distribution Detection by Learning From the Deployment
           Environment

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      Authors: Nathan Inkawhich;Jingyang Zhang;Eric K. Davis;Ryan Luley;Yiran Chen;
      Pages: 2070 - 2086
      Abstract: Recognition systems in the remote sensing domain often operate in “open-world” environments, where they must be capable of accurately classifying data from the in-distribution categories while simultaneously detecting and rejecting anomalous/out-of-distribution (OOD) inputs. However, most modern designs use deep neural networks (DNNs) to perform this recognition function that are trained under “closed-world” assumptions in offline-only environments. As a result, by construction, these systems are ill-posed to handle anomalous inputs and have no mechanism for improving OOD detection abilities during deployment. In this work, we address these weaknesses from two aspects. First, we introduce advanced DNN training methods to codesign for accuracy and OOD detection in the offline training phase. We then propose a novel “learn-online” workflow for updating the DNNs during deployment using a small library of carefully collected samples from the operating environment. To show the efficacy of our methods, we consider experimenting with two popular recognition tasks in remote sensing: scene classification in electro-optical satellite images and automatic target recognition in synthetic aperture radar imagery. In both, we find that our two primary design contributions can individually improve detection performance, while also being complementary. Additionally, we find that detection performance on difficult and highly granular OOD samples can be drastically improved using only tens or hundreds of samples collected from the environment. Finally, through analysis, we determine that the logic for adding/removing samples from the collection library is of key importance and using a proper learning rate during the model update step is critical.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Clustering Point Process Based Network Topology Structure Constrained
           Urban Road Extraction From Remote Sensing Images

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      Authors: You Wu;Quanhua Zhao;Zhaoyu Shen;Yu Li;
      Pages: 2087 - 2098
      Abstract: To extract complicated road network from remote sensing images on urban scenes, this article presents a clustering point process (CPP) based network topology structure constrained road extraction algorithm. Firstly, the CPP is constructed to model the feature points, such as endpoints, bends, and crossroads in a road system. Based on that, an initial network topology structure is constructed by connecting the points with lines. Then, according to the network structure characteristic and the spectral characteristic of road, a network topology structure constraining model and a spectral measurement constraining model are constructed, respectively. By combining the models above, a road extraction model is built under the framework of Bayes’ theorem. Finally, to simulate from the road extraction model and extract an optimal road network, a solution strategy, reversible jump Markov Chain Monte Carlo (RJMCMC) simulation algorithm with related transfer operations, is designed according to the CPP and network topology structure. Several high-resolution remote sensing images on urban scenes are tested. According to a buffer evaluation method, and compared with the comparing algorithms, accuracy and extraction rate of results from the proposed algorithm are increased by 10.86% and 8.75% on average, respectively. It is proved that the proposed algorithm can extract the complicated road network effectively.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Dual-Channel Fully Convolutional Network for Land Cover Classification
           Using Multifeature Information

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      Authors: Ziwei Liu;Mingchang Wang;Fengyan Wang;Xue Ji;Zhiguo Meng;
      Pages: 2099 - 2109
      Abstract: High-resolution remote sensing images have the advantage of timeliness, and they can display feature information in more detail. Deep learning embodies its unique characteristics in land cover classification, target recognition, and other fields, which can automatically learn the in-depth feature information of images and make accurate classification decisions. However, when deep learning models extract high-dimensional abstract feature information, they often ignore and lose part of the underlying features essential for classification accuracy. This article proposes a dual-channel fully convolutional network (D-FCN), whose two channels, respectively, take image data and low-level features such as color, texture, and shape as the different input data to combine the underlying features with high-dimensional abstract features. To reduce the complexity of the model, we add a large number of skip connections between the model and make full use of the advantages of weight sharing and local connections to connect spatial context information. We used multifeature information as the model input and compared and analyzed the impact of different features on the land cover classification accuracy, and finally obtained the most suitable combination of multifeature information. In addition, we provide a small-scale land cover classification dataset with labels to verify the applicability and transferability of the D-FCN, and use the optimal combination of multifeature information to conduct comparative experiments on the small-scale dataset. The experimental results show that D-FCN has outstanding applicability and transferability. Compared with other state-of-the-art models, D-FCN has a more challenging performance and greatly reduces model complexity.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multi-Scenario Simulation of Land Use for Sustainable Development Goals

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      Authors: Min Cao;Lijiao Chang;Shangjing Ma;Zijun Zhao;Kai Wu;Xue Hu;Qiushi Gu;Guonian Lü;Min Chen;
      Pages: 2119 - 2127
      Abstract: Considering the UN sustainable development goals (SDGs) released in 2015, this article constructed an SDG-oriented land use simulation (SDG-LUS) model incorporating SDG-oriented system dynamics (SDG-SD) and SDG-oriented cellular automata (SDG-CA), and utilized it to simulate land use changes in the Yangtze river delta region. The SDG-SD model was developed to predict the land use demands from 2021 to 2030 under the constraints of multiple SDG indicators, including economic indicators (SDG2.3.1 and SDG8.1.1), social indicators (SDG3.c.1, SDG4.1.2, SDG5.b.1, SDG9.C.1, SDG 9.1.2, SDG11.2.1, and SDG11.7.1) and environmental indicators (SDG6.3.1 and SDG11.6.2). Four sustainable development scenarios, including reference, economic development, environmental protection and social progress scenarios, were established by utilizing the index and indicator board of the SDG indicators in 2030. Then, the SDG-CA model was applied to spatialize and simulate the land use evolution from 2021 to 2030 under different sustainable development scenarios. The results validate the applicability of the SDG-LUS model, and confirm that scenario simulations of different sustainability levels are conducive to supporting the formulation of sustainable land use plans.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Pansharpening Based on Variational Fractional-Order Geometry Model and
           Optimized Injection Gains

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      Authors: Yong Yang;Hangyuan Lu;Shuying Huang;Weiguo Wan;Luyi Li;
      Pages: 2128 - 2141
      Abstract: Pansharpening techniques fuse the complementary information from panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution MS image. However, the majority of existing pansharpening techniques suffer from spectral distortion owing to the low correlation between the MS and PAN images, and difficulties in obtaining appropriate injection gains. To address these issues, this article presents a novel pansharpening method based on the variational fractional-order geometry (VFOG) model and optimized injection gains. Specifically, to improve the correlation between the PAN and MS images, the VFOG model is constructed to generate a refined PAN image with a similar spatial structure to the MS image, while maintaining the gradient information of the original PAN image. Furthermore, to obtain accurate injection gains, and considering that the vegetated and nonvegetated regions should be dissimilar, an optimized adaptive injection gain based on the normalized differential vegetation index is designed. The final pansharpened image is obtained by an injection model using the refined PAN image and optimized injection gains. Extensive experiments on various satellite datasets demonstrate that the proposed method offers superior spectral and spatial fidelity compared to existing state-of-the-art algorithms.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • ForkNet: Strong Semantic Feature Representation and Subregion Supervision
           for Accurate Remote Sensing Change Detection

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      Authors: Haoming He;Yerui Chen;Mingchao Li;Qiang Chen;
      Pages: 2142 - 2153
      Abstract: In this article, we propose an effective siamese feature pyramid network (FPN), ForkNet, for remote sensing change detection (RSCD). We find that the siamese network structure, which is widely used for RSCD, contains only one downsampling network in the feature extraction stage, e.g., VGG16 and ResNet-18, to extract the deep features of a single image, such that the features have a large semantic gap between high-level feature maps and low-level feature maps. The low-level feature maps with weak semantics may become a bottleneck of network performance. Thus, we apply an FPN to the feature extraction stage to generate feature representations with strong semantics at each level. Further, we design a cross-resolution attention module (CRAM) to aggregate contextual information across resolutions and naturally serve as a bridge for exchanging information across different resolution feature maps. The siamese FPN equipped with the CRAM is called ForkNet. To better train ForkNet, we extend the Tversky loss to a novel loss, pyramid Tversky loss, which is capable of supervising subregions at different scales to obtain more fine-grained detection results. Using pyramid Tversky loss together with Focal loss, our ForkNet achieves state-of-the-art detection performance on two challenging datasets.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multiscale Multiinteraction Network for Remote Sensing Image Captioning

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      Authors: Yong Wang;Wenkai Zhang;Zhengyuan Zhang;Xin Gao;Xian Sun;
      Pages: 2154 - 2165
      Abstract: Much of the recent work in remote sensing image captioning is influenced by natural image captioning. These methods tend to fix the defects of the model architecture to improve the previous work, but pay little attention to the differences between remote sensing images and natural images. By considering these differences, we propose a multiscale multiinteraction remote sensing image captioning model. As in Fig. 1(a), the targets in remote sensing images have a wide range of scales; while the natural images are generally taken close-up, resulting in a similar scale for the foreground targets. Due to the difference in shooting methods, the model pretrained on close-up natural images cannot capture multiscale remote sensing targets well. To alleviate this problem, we propose a two-stage multiscale structure for feature representation, where we first finetune the CNN backbone on remote sensing images for domain adaption, then we collect features from different stages as the multiscale feature representation. Moreover, due to the shooting distance, the height information of the target in the remote sensing image is greatly weakened, thus some objects like low plants and grasses become difficult to identify, as in Fig. 1(b). Thus, we further propose a multiinteraction feature representation module, where information flow of the same and different layers could effectively interact. By calculating the similarity score among features, we fuse features with high similarity, and increase the distance between features of different categories, thereby enhancing the distinguishability. Results on RSICD, Sydney-Captions, and UCM-Captions show a clear improvement over the compared methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Variabilities of Land Surface Temperature and Frontal Area Index Based on
           Local Climate Zone

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      Authors: Liguang Li;Ziqi Zhao;Hongbo Wang;Lidu Shen;Ningwei Liu;Bao-Jie He;
      Pages: 2166 - 2174
      Abstract: Urban temperature increase brings a challenge to public health, so urban warming has been widely concerned. Within the central area of Shenyang, a 3 × 3 fishnet with nine grids was built to analyze land surface temperature (LST) and frontal area index (FAI) variations based on the local climate zone (LCZ) classification and to explore relationships among them to generate implications for using wind for urban cooling. The results indicate that LST in the 4# grid (central grid) was the highest (35.11 °C) among the nine grids. In general, a higher LST occurred in the grid with a larger building area and vice versa. LCZ-G underwent the largest temperature reduction among the 17 LCZ types. LCZ-2 had the highest LST, while the LCZ-1 had the lowest LST among the three high and intensive building types, except for the LST in the 2# and 4# grids. The FAI in Shenyang decreased from the center to the surroundings. The FAI value was greatest in the 4# grid and the lowest was in the 2# and 8# grids under the prevailing wind of summer and winter. The FAI variations suggested that prevailing SW, SSW, and S winds in summer were difficult to penetrate the central urban area, and the wind cooling effect could not perform. The prevailing NEN and EEN winds in winter could enter the central urban area, enhancing the wind cooling effect, while it was a disadvantage in winter. Overall, there was a significant positive relationship among LST, FAI, and area of LCZ-building.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Downscaling ESA CCI Soil Moisture Based on Soil and Vegetation Component
           Temperatures Derived From MODIS Data

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      Authors: Chengyun Song;Guangcheng Hu;Yanli Wang;Xueshan Qu;
      Pages: 2175 - 2184
      Abstract: The European Space Agency's Climate Change Initiative (ESA CCI) soil moisture could provide long-time microwave-retrieved soil moisture data but is limited to regional applications due to the low resolution (25 km). A new method of downscaling ESA CCI soil moisture to 1 km is presented in this study. First, the soil and vegetation component temperatures (SVCT) were estimated using MODIS land surface temperature and normalized difference vegetation index (NDVI) data. Following this, the relationship between ESA CCI soil moisture and 1-km SVCT was constructed based on the negative linear relationship between the temperature vegetation dryness index (TVDI) and soil moisture. The dry and wet lines used to estimate TVDI need not to be obtained in the method. The coefficients were obtained directly from 25-km ESA CCI soil moisture and 1-km SVCT by the upscaling algorithm of soil moisture. The method was applied to the Naqu area on the Tibetan Plateau. Downscaled soil moisture was validated with ground measurements collected at five sites within the soil moisture/temperature monitoring network on the central Tibetan Plateau from May to October 2014. The results show that the trend of the time series of the downscaled soil moisture is similar to the ground measurements during this period, and the root-mean-square error is 0.0568 m3/m3. The method is suitable for the condition with an NDVI higher than 0.4. The key points of the approach are to obtain SVCT and the relationship between soil moisture and SVCT.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Method of High Signal-to-Noise Ratio and Wide Swath SAR Imaging Based on
           Continuous Pulse Coding

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      Authors: Jingyi Wei;Yachao Li;Rui Yang;Lianghai Li;Liang Guo;
      Pages: 2185 - 2196
      Abstract: In synthetic aperture radar (SAR), increasing the pulsewidth of signal is an effective way to achieve high signal-to-noise ratio (HSNR) imaging. However, when the pulse repetition frequency (PRF) is fixed, increasing the pulsewidth will reduce the maximum unambiguous swath width of SAR. In order to solve this contradiction, a method based on continuous pulse coding (CPC) which increases the average transmit power by multiple pulses with varying high PRF is proposed in this article. Due to the small interval between pulses, the echo will have range ambiguity and occlusion problems. To obtain the complete echo, we first use the form of CPC signal and the swath width of radar to construct a linear equation set to model the ambiguous echo of each receiving window. Next, the established linear equations are split according to the distribution law of receiving window. Finally, the echo energy accumulation of multiple pulses is accomplished by solving the split sublinear equations. Therefore, the echo with both swath width corresponding to narrow pulse and HSNR corresponding to wide pulse is obtained to realize HSNR and wide swath SAR imaging. The experimental results have confirmed the effectiveness of the method proposed in this article.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Tomographic Method for the Reconstruction of the Plasmasphere Based on
           COSMIC/ FORMOSAT-3 Data

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      Authors: Fabricio S. Prol;Mohammed Mainul Hoque;
      Pages: 2197 - 2208
      Abstract: A tomographic method has been developed for reconstructing the topside ionospheric and plasmaspheric electron density distribution using total electron content (TEC) measurements from global positioning system (GPS) receivers aboard the constellation observing system for meteorology, ionosphere, and climate/Formosa Satellite Mission 3 (COSMIC/FORMOSAT-3). Since the COSMIC/FORMOSAT-3 constellation has an orbit altitude of about 800 km, the integral TEC measurements obtained from the topside GPS navigation data are rather small and imposed relevant challenges to obtaining stable electron density reconstructions. However, the developed method can represent the natural variability of the plasma ambient in terms of latitude, altitude, solar activity, season, and local time when analyzing electron density reconstructions during 2008–2013. The method employs independent spatial grids for satellite rising and setting geometries and imposes a set of constraints to stabilize the solution in the presence of noise and ill-conditioned geometry. We further consider background ionosphere and plasmasphere models for electron density initialization and filling data gaps. The quality assessment using TEC and in-situ electron density measurements has shown that the proposed method performs better than the background model, with improvements of about 26% in TEC and 20% in terms of electron density. Our investigation also reveals the necessity of more accurate background electron density representations and precise TEC measurements in order to have better plasmaspheric specifications at high altitudes.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Knowledge-Based Morphological Deep Transparent Neural Networks for Remote
           Sensing Image Classification

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      Authors: Dasari Arun Kumar;
      Pages: 2209 - 2222
      Abstract: Land use/land cover classification of remote sensing images provide information to take efficient decisions related to resource monitoring. There exists several algorithms for remote sensing image classification. In the recent years, Deep learning models like convolution neural networks (CNNs) are widely used for remote sensing image classification. The learning and generalization ability of CNN, results in better performance in comparison with similar type of models. The functional behavior of CNNs is unexplainable because of its multiple layers of convolution and pooling operations. This results in black box characteristics of CNNs. Motivated with this factor, a CNN model with functional transparency is proposed in the present study. The model is named as Knowledge Based Morphological Deep Transparent Neural Networks (KBMDTNN) for remote sensing image classification. The architecture of KBMDTNN model provides functional transparency due to application of morphological operators, convolutional and pooling layers, and transparent neural network. In KBMDTNN model, the morphological operator preserve the shape/size information of the objects through efficient image segmentation. Convolution and pooling layers are used to produce minimal number of features from the image. The operational transparency of proposed model is coined based on the mathematical understanding of each layer in the model instead of randomly adding layers to the architecture of model. The transparency of proposed model is also because of assigning the initial weights of NN in output layer of model with computed values instead of random values. The proposed KBMDTNN model outperformed similar type of models as tested with multispectral and hyperspectral remote sensing images. The performance of KBMDTNN model is evaluated with the metrics like overall accuracy (OA), overall accuracy standard deviation ($OA_{text{STD}}$), produce-’s accuracy (PA), user’s accuracy (UA), dispersion score (DS), and kappa coefficient (KC).
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Homo–Heterogenous Transformer Learning Framework for RS Scene
           Classification

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      Authors: Jingjing Ma;Mingteng Li;Xu Tang;Xiangrong Zhang;Fang Liu;Licheng Jiao;
      Pages: 2223 - 2239
      Abstract: Remote sensing (RS) scene classification plays an essential role in the RS community and has attracted increasing attention due to its wide applications. Recently, benefiting from the powerful feature learning capabilities of convolutional neural networks (CNNs), the accuracy of the RS scene classification has significantly been improved. Although the existing CNN-based methods achieve excellent results, there is still room for improvement. First, the CNN-based methods are adept at capturing the global information from RS scenes. Still, the context relationships hidden in RS scenes cannot be thoroughly mined. Second, due to the specific structure, it is easy for normal CNNs to exploit the heterogenous information from RS scenes. Nevertheless, the homogenous information, which is also crucial to comprehensively understand complex contents within RS scenes, does not get the attention it deserves. Third, most CNNs focus on establishing the relationships between RS scenes and semantic labels. However, the similarities between them are not considered deeply, which are helpful to distinguish the intra-/interclass samples. To overcome the limitations mentioned previously, we propose a homo–heterogenous transformer learning (HHTL) framework for the RS scene classification in this article. First, a patch generation module is designed to generate homogenous and heterogenous patches. Then, a dual-branch feature learning module (FLM) is proposed to mine homogenous and heterogenous information within RS scenes simultaneously. In the FLM, based on vision transformer, not only the global information but also the local areas and their context information can be captured. Finally, we design a classification module, which consists of a fusion submodule and a metric-learning module. It can integrate homo–heterogenous information and compact/separate samples from the same/different RS scene categories. Extensive experiments are conducted on four p-blic RS scene datasets. The encouraging results demonstrate that our HHTL framework can outperform many state-of-the-art methods. Our source codes are available at the below website.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Few-Shot Transfer Learning for SAR Image Classification Without Extra SAR
           Samples

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      Authors: Yuan Tai;Yihua Tan;Shengzhou Xiong;Zhaojin Sun;Jinwen Tian;
      Pages: 2240 - 2253
      Abstract: Deep learning-based synthetic aperture radar (SAR) image classification is an open problem when training samples are scarce. Transfer learning-based few-shot methods are effective to deal with this problem by transferring knowledge from the electro–optical (EO) to the SAR domain. The performance of such methods relies on extra SAR samples, such as unlabeled novel class’s samples or labeled similar classes samples. However, it is unrealistic to collect sufficient extra SAR samples in some application scenarios, namely the extreme few-shot case. In this case, the performance of such methods degrades seriously. Therefore, few-shot methods that reduce the dependence on extra SAR samples are critical. Motivated by this, a novel few-shot transfer learning method for SAR image classification in the extreme few-shot case is proposed. We propose the connection-free attention module to selectively transfer features shared between EO and SAR samples from a source network to a target network to supplement the loss of information brought by extra SAR samples. Based on the Bayesian convolutional neural network, we propose a training strategy for the extreme few-shot case, which focuses on updating important parameters, namely the accurately updating important parameters. The experimental results on the three real-SAR datasets demonstrate the superiority of our method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Novel 3-D Local DAISY-Style Descriptor to Reduce the Effect of Point
           Displacement Error in Point Cloud Registration

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      Authors: Fariborz Ghorbani;Hamid Ebadi;Amin Sedaghat;Norbert Pfeifer;
      Pages: 2254 - 2273
      Abstract: Three-dimensional (3-D) point clouds are widely considered for applications in different fields. Various methods have been proposed to generate point cloud data: LIDAR and image matching from static and mobile platforms, including, e.g., terrestrial laser scanning. With multiple point clouds from stationary platforms, point cloud registration is a crucial and fundamental issue. A standard approach is a point-based registration, which relies on pairs of corresponding points in two-point clouds. Therefore, a necessary step in point-based registration is the construction of 3-D local descriptors. One of the (many) challenges that will specifically affect the performance of local descriptors with local spatial information is the point displacement error. This error is caused by the difference in the distributions of points surrounding a (potentially) corresponding center point in the two-point clouds. It can occur for various reasons such as 1) distortions caused by the sensors recording the data, 2) moving objects, 3) varying density of point cloud, 4) change of viewing angle, and 5) different of the sensors. The purpose of this article is to develop a new 3-D local descriptor reducing the effect of this type of error in point cloud coarse registration. The approach includes an improved local reference frame and a new geometric arrangement in point cloud space for the 3-D local descriptor. Inspired by the 2-D DAISY descriptor, a geometric arrangement is created to reduce the effect of the point displacement error. in addition, directional histograms are considered as features. Investigations are performed for point clouds from challenging environments, which are publicly available. The results of this study show the high performance of the proposed approach for point cloud registration, especially in more challenging and noisy environments.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Automated Geological Landmarks Detection on Mars Using Deep Domain
           Adaptation From Lunar High-Resolution Satellite Images

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      Authors: Rasha Alshehhi;Claus Gebhardt;
      Pages: 2274 - 2283
      Abstract: The diversity in geological characteristics on the planetary surface, such as distribution (density), size, shapes, floor structures, ages, and availability of various input data types such as optical, thermal images, and digital elevation maps pose numerous challenges for detecting geological landmarks (e.g., rockfalls, craters, etc.). Several automatic detection methods are proposed to identify geological landmarks. However, the insufficiency of the labeled dataset is a challenging problem. It requires exceedingly time-consuming and expensive manual annotation. In this article, we use the domain adaptation technique to transfer deep learning from the planetary surface to another (lunar surface into Martian surface). We test the feasibility of transfer learning of the convolutional neural networks in optical images and elevation maps to distinguish landmarks such as rockfalls and craters from the background. The experimental results demonstrate the effectiveness of the proposed method. It achieves high F1-scores compared to the state-of-the-art methods with 58.32$,{pm},$2.3 and 57.51$,{pm},$2.4 in detecting rockfall regions in optical lunar and Martian images. It also achieves 65.32$,{pm},$1.8, 67.39$,{pm},$2.4, 77.37$,{pm},$2.2, and 72.56$,{pm},$2.3 in detecting crater regions in optical images and digital elevation maps of Moon and Mars. This method can be a potential approach to identify landmarks for coming Mars missions.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Classification of Laser Footprint Based on Random Forest in Mountainous
           Area Using GLAS Full-Waveform Features

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      Authors: Xiangfeng Liu;Xiaodan Liu;Zhenhua Wang;Genghua Huang;Rong Shu;
      Pages: 2284 - 2297
      Abstract: Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%, 97.73%, 98.30%, 97.99&#x-025;, and 0.9737, respectively; while 83.45%, 82.55%, 82.98%, 81.16%, and 0.7765 for NB, which has the worst performance. LR performs better than RBF-SVM, linear-SVM, and KNN. It also observes worse classification accuracy for all methods when the waveforms are more complex.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Performance Improvement for SAR Tomography Based on Local Plane Model

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      Authors: Wenkang Liu;Alessandra Budillon;Vito Pascazio;Gilda Schirinzi;Mengdao Xing;
      Pages: 2298 - 2310
      Abstract: Multilook approaches have been applied in synthetic aperture radar (SAR) tomography (TomoSAR), for improving the density and regularity of persistent scatterers reconstructed from multipass SAR images in both rural and urban regions. Multilook operations assume that all scatterers in a given neighborhood are similar in height, thereby providing additional data for recovering the position and reflectivity of a single scatterer, so that a higher signal-to-noise ratio can be achieved. This is equivalent to assuming that scatterers belonging to a local neighborhood of range–azimuth cells are located on horizontal planes. The present article generalizes this approach by adopting the so-called local plane (LP) model for TomoSAR imaging in urban areas, accounting for local variations in the height of scatterers that are not negligible. Furthermore, an LP-generalized likelihood ratio test (LP-GLRT) algorithm is developed to implement the previous idea. Compared with the multilook generalized likelihood ratio test algorithm, LP-GLRT shows better performance in the case of urban structures and terrains in experiments based on both simulated data and TerraSAR-X images.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Toward Knowledge Extraction in Classification of Volcano-Seismic Events:
           Visualizing Hidden States in Recurrent Neural Networks

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      Authors: Manuel Titos;Luz García;Milad Kowsari;Carmen Benítez;
      Pages: 2311 - 2325
      Abstract: Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering what and how recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation. The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. A representative dataset from the deception island volcano (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • OmbriaNet—Supervised Flood Mapping via Convolutional Neural Networks
           Using Multitemporal Sentinel-1 and Sentinel-2 Data Fusion

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      Authors: Georgios I. Drakonakis;Grigorios Tsagkatakis;Konstantina Fotiadou;Panagiotis Tsakalides;
      Pages: 2341 - 2356
      Abstract: Regions around the world experience adverse climate-change-induced conditions that pose severe risks to the normal and sustainable operations of modern societies. Extreme weather events, such as floods, rising sea levels, and storms, stand as characteristic examples that impair the core services of the global ecosystem. Especially floods have a severe impact on human activities, hence, early and accurate delineation of the disaster is of top priority since it provides environmental, economic, and societal benefits and eases relief efforts. In this article, we introduce OmbriaNet, a deep neural network architecture, based on convolutional neural networks, that detects changes between permanent and flooded water areas by exploiting the temporal differences among flood events extracted by different sensors. To demonstrate the potential of the proposed approach, we generated OMBRIA, a bitemporal and multimodal satellite imagery dataset for image segmentation through supervised binary classification. It consists of a total number of 3.376 images, synthetic aperture radar imagery from Sentinel-1, and multispectral imagery from Sentinel-2, accompanied with ground-truth binary images produced from data derived by experts and provided from the Emergency Management Service of the European Space Agency Copernicus Program. The dataset covers 23 flood events around the globe, from 2017 to 2021. We collected, co-registrated and preprocessed the data in Google Earth Engine. To validate the performance of our method, we performed different benchmarking experiments on the OMBRIA dataset and we compared with several competitive state-of-the-art techniques. The experimental analysis demonstrated that the proposed formulation is able to produce high-quality flood maps, achieving a superior performance over the state-of-the-art. We provide OMBRIA dataset, as well as OmbriaNet code at: https://github.com/geodrak/OMBRIA.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Siamese Network Based U-Net for Change Detection in High Resolution
           Remote Sensing Images

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      Authors: Tao Chen;Zhiyuan Lu;Yue Yang;Yuxiang Zhang;Bo Du;Antonio Plaza;
      Pages: 2357 - 2369
      Abstract: Remote sensing image change detection (RSICD) is a technique that explores the change of surface coverage in a certain time series by studying the difference between multiple remote sensing images (RSIs) collected over the same area. Traditional RSICD algorithms exhibit poor performance on complex change detection (CD) tasks. In recent years, deep learning (DL) techniques have achieved outstanding results in the fields of RSI segmentation and target recognition. In CD research, most of the methods treat multitemporal remote sensing data as one input and directly apply DL-based image segmentation theory on it while ignoring the spatio-temporal information in these images. In this article, a new siamese neural network is designed by combing an attention mechanism (Siamese_AUNet) with UNet to solve the problems of RSICD algorithms. SiameseNet encodes the feature extraction of RSIs by two branches in the siamese network, respectively. The weights are shared between these two branches in siamese networks. Subsequently, an attention mechanism is added to the model in order to improve its detection ability for changed objects. The models are then compared with conventional neural networks using three benchmark datasets. The results show that the Siamese_AUNet newly proposed in this article exhibits better performance than other standard methods when solving problems related to weak CD and noise suppression.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Inter-Comparison of Proximal Near-Surface Soil Moisture Measurement
           Techniques

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      Authors: Xiaoling Wu;Jeffrey P. Walker;François Jonard;Nan Ye;
      Pages: 2370 - 2378
      Abstract: Precision agriculture is experiencing substantial development through the improved availability of cost-effective instruments for data collection. This includes ground-based proximal sensing technologies that are able to compete with satellite and aircraft observation systems, due to low operational costs, high operational flexibility, and high spatial resolution. This article was therefore designed to compare the performance of multiple sensing systems mounted on a smart buggy platform. A number of proximal sensing technologies were then evaluated and intercompared for their accuracy in retrieving high resolution near-surface soil moisture. The sensors tested included an L-band microwave radiometer (ELBARA III), a global navigation satellite system reflectometer sensor (LARGO), and an electromagnetic induction sensor (EM38). Data were collected during the fifth Soil Moisture Active Passive Experiment (SMAPEx-5) in Yanco, NSW, Australia, in September 2015. Observations from each sensor were converted to surface soil moisture values which were in turn evaluated against reference measurements obtained by in situ soil moisture measurements. The sensing technologies tested here have been individually assessed by many other studies, but within different regions and environments including surface condition, local weather, observing height, size of footprint, etc. Consequently, this article has used a single platform to intercompare the different sensors to be evaluated concurrently. Results from this article indicated that the L-band microwave radiometer achieved the best performance in retrieving surface soil moisture. The average RMSE and R were found to be 0.055 cm3/cm3 and 0.68 for ELBARA III, 0.084 cm3/cm3 and 0.51 for LARGO, and 0.090 cm3/cm3 and 0.32 for the EM38.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Low-Power Hyperspectral Anomaly Detector Implementation in Cost-Optimized
           FPGA Devices

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      Authors: Julián Caba;María Díaz;Jesús Barba;Raúl Guerra;Soledad Escolar;Sebastián López;
      Pages: 2379 - 2393
      Abstract: Onboard data processing for on-the-fly decision-making applications has recently gained momentum in the field of remote sensing. In this context, hyperspectral anomaly detection has received special attention since its main purpose lies in the identification of abnormal events in an unsupervised manner. Nevertheless, onboard real-time hyperspectral image processing still poses several challenges before becoming a reality. This is why there is an emerging trend toward the development of hardware-friendly algorithmic solutions embedded in reconfigurable devices. In this context, this work contributes to a hardware architecture that ensures a progressive line processing in time-sensitive applications limited by the scarcity of hardware resources. In this sense, we have implemented the state-of-the-art hardware-friendly line-by-line fast anomaly detector for hyperspectral imagery (HW-LbL-FAD) detector on a reconfigurable hardware for a real-time performance. Specifically, we have selected a cost-optimized field-programmable gate array (ZC7Z020-CLG484) to implement our solution whose results draw up a good tradeoff between the following three features: time performance, energy consumption, and cost. The experimental results indicate that our hardware component is able to process hyperspectral images of 825x1024 pixels and 160 bands in 0.51 s with a power budget of 1.3 W and costs around 150€.Regarding detection performance, the HW-LbL-FAD algorithm outperforms other state-of-the-art algorithms.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimation of Root Zone Soil Moisture Profile by Reduced-Order Variational
           Data Assimilation Using Near Surface Soil Moisture Observations

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      Authors: Parisa Heidary;Leila Farhadi;Muhammad Umer Altaf;
      Pages: 2394 - 2409
      Abstract: Soil moisture plays an important role in the global water cycle and has an important impact on energy fluxes at the land surface. It also defines the initial and boundary condition of terrestrial hydrological processes, including infiltration, runoff, and evapotranspiration. Therefore, accurate estimation of soil moisture pattern is of critical importance. Satellite-based soil moisture can be obtained with well-defined temporal and spatial resolutions and with global coverage. However, they only provide surface soil moisture at the upper few centimeters of the soil column. Soil moisture simulation models can produce estimates of soil moisture profile up to several meters of depth in different time steps. However, uncertainty in model parameters (e.g., unknown initial soil moisture profile) and meteorological forcing can substantially alter the accuracy of the model estimates. In this article, the potential of using surface soil moisture measurements to retrieve the initial soil moisture profile will be explored in a synthetic study, using two proposed reduced-order variational data assimilation (VDA) techniques and a simple 1-D soil moisture model. The accuracy and feasibility of the proposed approaches are confirmed by comparing the initial soil moisture profiles estimated using the proposed reduced-order VDA techniques versus the full-adjoint VDA technique. Results illustrated that the reduced-order VDA techniques can estimate initial soil moisture profile from near surface soil moisture observations with the comparable level of accuracy as full-adjoint VDA. The effectiveness of the reduced-order VDA in retrieving the initial soil moisture profile is further demonstrated by assimilating surface soil moisture into HYDRUS-1D, mimicking real-world errors.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Integrating Zhuhai-1 Hyperspectral Imagery With Sentinel-2 Multispectral
           Imagery to Improve High-Resolution Impervious Surface Area Mapping

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      Authors: Xiaoxiao Feng;Zhenfeng Shao;Xiao Huang;Luxiao He;Xianwei Lv;Qingwei Zhuang;
      Pages: 2410 - 2424
      Abstract: Mapping impervious surface area (ISA) is essential for many fields and applications, such as urban heat island, urban planning and management. However, the complex urban landscapes pose challenges in accurately retrieving ISA. Spaceborne hyperspectral (HS) remote sensing imagery provides rich spectral information with short revisit cycles, making it an ideal data source for ISA extraction. Nevertheless, insufficient single-band energy, the involvement of modulation transfer function, and the low signal-to-noise ratio of spaceborne HS imagery usually result in poor image clarity and noises, leading to inaccurate restuls. To address this challenge, we propose a new deep feature fusion-based classification method to improve 10-m resolution ISA mapping by integrating Zhuhai-1 HS imagery with Sentinel-2 multispectral (MS) imagery. We extract deep features from MS and HS imagery via 2D convolutional neural network (CNN), aiming to increase feature diversity and improve the models recognition capability. MS imagery is used to enhance the spatial information of the HS image, improving the ISA retrieval by reducing the impact of noises. By combining the deep features, we obtain joint spatial-spectral features, leading to high classification accuracy and robustness. We test the proposed method in two highly urbanized areas that cover Foshan city and Wuhan city. The results reveal that the proposed method obtains an OA of 96.72% and 96.75% in two study areas, 18.78% and 8.66% higher than classification results with only HS imagery as input. The final ISA extraction OA is 95.42% and 95.50% in the two study areas, its the highest among the comparison methods
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Direct Comparison of Sea Surface Velocity Estimated From Sentinel-1 and
           TanDEM-X SAR Data

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      Authors: Anis Elyouncha;Leif E. B. Eriksson;Harald Johnsen;
      Pages: 2425 - 2436
      Abstract: This article presents the first direct comparison of the sea surface radial velocity (RVL) derived from the two satellite SAR systems Sentinel-1 and TanDEM-X, operating at different frequencies and imaging modes. The RVL is derived from the Doppler centroid (Dc) provided in the Sentinel-1 OCN product and from the along-track interferometric phase of the TanDEM-X. The comparison is carried out using unique opportunistic acquisitions, collocated in space and time, over three different sites located in the Iceland Sea, the Pentland Firth, and the Kattegat Sea. First, it is observed that the RVL derived from both satellites is biased, thus calibration is applied using the land as a reference. The comparison shows that the correlation and the mean bias between the two datasets depend on the differences in acquisition time, incidence angle, and azimuth angle, and on wind and surface velocities. It is found that, given a time difference of $lesssim$ 20 min, the spatial correlation coefficient is relatively high (between 0.70 and 0.93), which indicates that the two SAR systems observe similar sea surface current fields. The spatial correlation degrades primarily due to increasing time difference and decreasing velocity magnitudes. It is also found that the mean RVL bias increases primarily with the radial wind speed, which suggests that the bias is mainly due to the wave-induced Doppler shift. This article shows that under certain conditions, i.e., similar acquisition geometry and short time delay, a good agreement between the two independently derived RVL is achieved, both in the spatial variation and absolute mean value. This encourages a synergistic use of the sea surface velocity estimated from different C- and X-band SAR systems.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Near Real-Time InSAR Deformation Time Series Estimation With Modified
           Kalman Filter and Sequential Least Squares

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      Authors: Baohang Wang;Qin Zhang;Chaoying Zhao;Antonio Pepe;Yufen Niu;
      Pages: 2437 - 2448
      Abstract: The current and planned synthetic aperture radar (SAR) sensors mounted on satellite platforms will continue to operate over the coming years, providing unprecedented SAR data for monitoring wide-range surface deformations. The near real-time processing of SAR interferometry (InSAR) data for the retrieval of ground-deformation time series is urgently required in the current era of big data. The state-of-the-art Kalman filter (KF) and sequential least squares (SLS) algorithms have been proposed to update an InSAR-driven ground-deformation time series. As a contribution of this study, we customize the conventional KF and SLS for big InSAR data for near real-time processing. The development of an accurate prediction model for KF-based InSAR processing is a challenge owing to the large scale of the targets for surface monitoring. We developed a modified KF algorithm, abbreviated as npKF, that does not require any prediction information, abbreviated as npKF. In this context, to avoid occupying a large storage space in SLS-based InSAR processing, we developed a modified SLS algorithm with a truncated cofactor matrix, abbreviated as TSLS. Using both simulated and actual SAR data, we evaluated the performance of these methods under three different aspects: accuracy, computation, and storage performance. With big data, the proposed method can estimate the deformation time series in near real time. It will be a reliable and effective tool for producing near real-time InSAR deformation products in the coming era of processing big SAR data and will play a part in the geologic hazard routine monitoring and early warning system.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Nearshore Bathymetry Based on ICESat-2 and Multispectral Images:
           Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

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      Authors: Xiaohan Zhang;Yifu Chen;Yuan Le;Dongfang Zhang;Qian Yan;Yusen Dong;Wei Han;Lizhe Wang;
      Pages: 2449 - 2462
      Abstract: Accurate bathymetric maps are essential to understand marine and coastal ecosystems. With the development of satellite and sensor technology, satellite-derived bathymetry (SDB) has been widely used to measure the depth of nearshore waters. Employment of physics-based methods requires a series of optical parameters of the water column and seafloor, which limits the application of these methods to shallow-water bathymetry. Due to convenience, low costs, and high efficiency, empirical methods based on in situ measurements and satellite imagery are increasingly used for nearshore bathymetry. These measurements are required to calibrate empirical models, so that reasonable accuracy can be achieved. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with the advanced topographic laser altimeter system, provides a novel opportunity for nearshore bathymetry. Using the new measurement strategy of photon counting, ICESat-2 can provide accurate bathymetric points from spaceborne observations, which can be used in place of in situ water depth data. In this study, ICESat-2 bathymetric points and multispectral images were used to train four typical models and produce bathymetric maps for Shanhu Island, Ganquan Island, and Lingyang Reef in the Xisha Islands of China. We evaluated the bathymetric results by comparing them with reference depth data from airborne light detection and ranging. All models had a satisfactory accuracy, as well as multimodel and multisource image consistency. With the ICESat-2 bathymetric points, SDB is no longer limited by in situ measurements. Hence, this approach could be extended to a larger scale to obtain nearshore bathymetric maps of coastal areas, surrounding islands, and reefs using free and open-access satellite data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • An Efficient Full-Aperture Approach for Airborne Spotlight SAR Data
           Processing Based on Time-Domain Dealiasing

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      Authors: Mingshan Ren;Heng Zhang;Weidong Yu;Zhen Chen;Hua Li;
      Pages: 2463 - 2475
      Abstract: The two-step approach (TSA) is an efficient full-aperture method to solve the Doppler spectrum aliasing when processing data acquired by a synthetic aperture radar (SAR) system operating in beam steering mode. This simple approach solves the spectral aliasing by implementing an azimuth convolution between the raw data and a chirp signal, thus avoiding complex subaperture division and recombination of subaperture approaches. However, as aliasing reoccurs in the slow time-domain after the convolution, the estimation and compensation for residual motion error by autofocus cannot be performed immediately, which is crucial for airborne and high-resolution spaceborne SAR processing. The extended TSA solves this problem by implementing full-aperture azimuth scaling, but it is inefficient when the scaling factor deviates from unity severely, which is common in the data acquisition geometry of an airborne SAR. Aiming at addressing the existing shortcomings, this article combines the TSA with autofocus by the time-domain dealiasing and provides an efficient full-aperture processing framework of airborne spotlight SAR data. Simulated and experimental airborne spotlight SAR data with the transmission signal bandwidth of 1.2 GHz and a coherent integration angle of 15$^circ$ are processed by the proposed algorithm and the clarity of the processed results shows its effectiveness.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Focusing High Maneuvering Bistatic Forward-Looking SAR With Stationary
           Transmitter Using Extended Keystone Transform and Modified Frequency
           Nonlinear Chirp Scaling

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      Authors: Jiabao Ding;Yachao Li;Ming Li;Jiadong Wang;
      Pages: 2476 - 2492
      Abstract: This article presents an imaging solution for high maneuvering bistatic forward-looking SAR with stationary transmitter (STHM-BFSAR). In STHM-BFSAR configuration that the transmitter is mounted on a stationary platform in side-looking mode while the receiver does high-speed maneuvering in forward-looking mode, high speed and acceleration induced by high dynamic characteristics of receiver in both along-track and height direction cause larger range cell migration (RCM) and more severe 2-D spatial variation of Doppler characteristics, which makes it more difficult to obtain well-focused bistatic SAR image. Furthermore, different from the general airborne bistatic SAR, STHM-BFSAR has a larger azimuth cubic phase term that exceeds $pi /4$ rad, which seriously affects the image quality. To deal with these problems, an imaging algorithm based on extended keystone transform (EKT) and modified frequency nonlinear chirp scaling (FNCS) is proposed in this article. EKT can correct spatial variant range curvature and bulk linear RCM, and the residual RCM is smaller than traditional keystone transform. The proposed modified FNCS is used to equalize the azimuth-range-dependent Doppler parameters in the frequency domain, which produces lower side-lobes and higher accuracy by compensating for the second-order spatial variation of the azimuth cubic coefficients. The final simulation results in this article verify the effectiveness of the proposed algorithm.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • River Surface Analysis and Characterization Using FMCW Radar

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      Authors: Marc A. Mutschler;Philipp A. Scharf;Patrick Rippl;Timo Gessler;Thomas Walter;Christian Waldschmidt;
      Pages: 2493 - 2502
      Abstract: Today’s warning systems for floods or droughts require sensor applications that provide a vast array of information. Current systems provide insight into either the water level or the river velocity. In order to obtain additional parameters for the characterization of the flow behavior in a noncontact manner, a frequency-modulated continuous-wave radar with chirp sequence is used. Since these sensors provide range and velocity information but also enable gathering of additional parameters. The presented measurements in this contribution were performed at four different rivers with a commercially available radar sensor. The results of the classical postprocessed 2-D fast Fourier transform are used as basis for an imagewise processing approach to obtain additional features for classifying the behavior of the river surface. For this purpose, the framewise enveloping velocities are depicted in a time sequence. Due to processing the detected reflection patterns in relation to time, a characteristic pattern for river flow profiles can be extracted. By reducing the information using time averaging, characteristic features for different flows can be extracted from the spatial envelope velocity distribution. In particular, the resulting insights lead to characteristic features for single flow distributions that enable novel monitoring systems with new possibilities to classify rivers using radar sensors.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • $k$ -Space+Decomposition+for+Millimeter+Wave+Short-Range+Radar&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&rft.date=2022&rft.volume=15&rft.spage=2503&rft.epage=2518&rft.aulast=Kidera;&rft.aufirst=Takeru&rft.au=Takeru+Ando;Shouhei+Kidera;">Accurate Micro-Doppler Analysis by Doppler and $k$ -Space Decomposition
           for Millimeter Wave Short-Range Radar

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      Authors: Takeru Ando;Shouhei Kidera;
      Pages: 2503 - 2518
      Abstract: This study presents a highly accurate range and Doppler-velocity extraction scheme for millimeter-wave (MMW) short-range sensing using the Doppler-velocity and $k$-space decomposition in a weighted kernel density (WKD) scheme. The WKD method has been developed as one of the most promising micro-Doppler analysis methods for human motion; however, an original WKD method requires a highly decomposed range profile to achieve its maximum performance. As the main contribution of this article, the proposed method introduces the Doppler velocity and k-space decomposition via the 4-D fast Fourier-transform process, which significantly improves the range resolution and reduces computational complexity. The numerical and experimental results show that the proposed method achieves significantly higher range and velocity accuracy and resolution, as well as higher noise-robustness at a lower computational cost.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Small Vessel Detection Based on Adaptive Dual-Polarimetric Feature Fusion
           and Sea–Land Segmentation in SAR Images

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      Authors: Yongsheng Zhou;Feixiang Zhang;Fei Ma;Deliang Xiang;Fan Zhang;
      Pages: 2519 - 2534
      Abstract: Detection of small sea vessels in synthetic aperture radar (SAR) images has received much attention in recent years because the small vessels have weak scattering intensity and few image pixels. The existing detection network structures are not well adapted to small-scale targets, the polarimetric data are not properly utilized, and the sea–land segmentation process to remove land false alarms is time-consuming. Regarding these problems, first, a single low-level path aggregation network is designed specifically for small targets. The structure reduces false alarms at the feature level by finding suitable single-scale feature maps for detection and adding a semantic enhancement module. Second, adaptive dual-polarimetric feature fusion is proposed to filter the multichannel features acquired by dual-polarimetric decomposition to reduce feature redundancy. Third, a segmentation layer is added to the network to shield the land from false alarms. The detection and segmentation layers share the feature extraction and feature fusion modules and are jointly trained by a joint loss. Finally, polarimetric SAR detection and segmentation dataset containing small vessel detection and sea–land segmentation labels is created with reference to the LS-SSDDv1.0 dataset, and experimental results on this dataset verify the improvement of this proposed method over other typical methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Anchor-Free Arbitrary-Oriented Object Detector Using Box Boundary-Aware
           Vectors

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      Authors: Donghang Yu;Qing Xu;Haitao Guo;Junfeng Xu;Jun Lu;Yuzhun Lin;Xiangyun Liu;
      Pages: 2535 - 2545
      Abstract: Characterized by complicated backgrounds, various types, large size variations, and arbitrary orientations, the detection and recognition of arbitrary-oriented objects in remote sensing images are challenging. To address the aforementioned problem, an anchor-free arbitrary-oriented object detector using box boundary-aware vectors is proposed. With the idea of CenterNet to detect objects as points, oriented object detection is achieved by predicting the center, the box boundary-aware vectors, the size, and the type of the bounding box. In the feature extraction stage of the designed architecture, Res2Net, a multiscale convolutional neural network, is used to extract feature maps of different scales and adaptively spatial feature fusion is adopted to improve the detector's adaptability to objects of different sizes. In the detector, a context enhancement module with a multibranch network is designed to enhance the contextual information of the objects and improve the detector's robustness to the complicated backgrounds. Experiments are carried on three challenging benchmarks (i.e., HRSC2016, UCAS-AOD, and DOTA) and our method achieves state-of-the-art performance with 90.30%, 89.70%, and 77.18% mAP, respectively.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Remote Sensing Image Scene Classification by Multiple Granularity Semantic
           Learning

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      Authors: Weilong Guo;Shengyang Li;Jian Yang;Zhuang Zhou;Yunfei Liu;Junjie Lu;Longxuan Kou;Manqi Zhao;
      Pages: 2546 - 2562
      Abstract: Remote sensing image scene classification faces challenges, such as the difference in semantic granularity of different scene categories and the imbalance of the number of samples, which cause the wrong features learning for deep convolutional networks (DCNs). This article proposes a multiple granularity semantic learning network (MGSN), including multiple granularity semantic learning (MGSL) and nonuniform sampling augmentation (NUA) modules. Specifically, the MGSL module makes full use of different granularities of semantic information of scenes, guiding the network to learn global and local features simultaneously. And, the relationship between semantic features of different granularity has been explored, based on which the learning of coarse-grained features helps to improve the learning of fine-grained semantic features. It shows that learning fine-grain semantics can inhibit learning coarse-grain semantic features. The NUA module combines sampling and sample augmentation to balance the sample distribution, which can avoid overfitting caused by oversampling. The proposed MGSN achieved state-of-the-art classification accuracy on two large-scale remote sensing image scene classification datasets, Million-AID and NWPU-RESISC45. Under 10$%$ and 20$%$ training samples of the NWPU-RESISC45 dataset, MGSN achieves 91.92$%$ and 94.33$%$ top-1 accuracy, respectively. In experiments conducted on the Million-AID dataset, the proposed MGSN performed best among 18 DCNs. In comparison to the baseline, FixEfficientNet, MGSN improved the accuracy of top-1 and top-5 by 10.63$%$ and 5.47$%$, respectively, with low complexity costs.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Spatially and Semantically Enhanced Siamese Network for Semantic Change
           Detection in High-Resolution Remote Sensing Images

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      Authors: Manqi Zhao;Zifei Zhao;Shuai Gong;Yunfei Liu;Jian Yang;Xiong Xiong;Shengyang Li;
      Pages: 2563 - 2573
      Abstract: Given a pair of bitemporal very high resolution (VHR) remote sensing images, the semantic change detection task aims to locate land surface changes and identify their semantic classes. The existing algorithms use independent branches to locate and identify separately without considering the association between branches. In this article, we propose an end-to-end spatially and semantically enhanced Siamese network (SSESN) for semantic change detection. The SSESN aggregates the rich spatial and semantic information in the VHR image through a designed spatial and semantic feature aggregation module. Additionally, a change-aware module is proposed to decouple the aggregated features. Features in the binary branch are fused to the semantic branches as prior location information. This allows the spatially enhanced features to predict changed regions and the semantically enhanced features to refine the region categorizations. Experimental results show that our method provides comparable results with the state-of-the-art binary change detection and semantic change detection algorithms.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Watershed-Based Attribute Profiles With Semantic Prior Knowledge for
           Remote Sensing Image Analysis

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      Authors: Deise Santana Maia;Minh-Tan Pham;Sébastien Lefèvre;
      Pages: 2574 - 2591
      Abstract: In this article, we develop a novel feature extraction method that combines two well-established mathematical morphology concepts: watersheds and morphological attribute profiles (APs). In order to extract spatial-spectral features from remote sensing data, APs were originally defined as sequences of filtering operators on inclusion trees, i.e., the max- and min-trees, computed from the input image. In this study, we extend the AP paradigm to the more general framework of hierarchical watersheds. Moreover, we explore the semantic knowledge provided by labeled training pixels during different phases of the watershed-AP construction, namely within the construction of hierarchical watersheds from the raw image and later within the filtering of the resulting hierarchy. We illustrate the relevance of the proposed method with two applications including land cover classification and building extraction using optical remote sensing images. Experimental results show that the new profiles outperform various existing features using two public datasets (Zurich and Vaihingen), thus providing another high potential feature extraction method within the AP family.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Multiparameter Adaptive Target Classification Using Full-Polarimetric GPR:
           A Novel Approach to Landmine Detection

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      Authors: Haoqiu Zhou;Xuan Feng;Zejun Dong;Cai Liu;Wenjing Liang;
      Pages: 2592 - 2606
      Abstract: Full-polarimetric ground penetrating radar (FP-GPR) can measure the ability of an object to change the polarization of electromagnetic waves. Compared to the traditional GPR, it has a stronger capability to identify underground objects. In recent years, a series of polarization decomposition methods have been applied to the FP-GPR data processing to obtain the polarimetric attributes and enhance the capability of targets identification. Different polarimetric attributes characterize different features of a target but there is still no effective way to integrate these attributes and take their respective advantages for target classification. In this article, we propose a particle center AdaBoost (PCAD) method and achieve the multiparameter adaptive target identification. The experimental results indicate that the PCAD method can automatically select suitable parameters during the training process for different targets. Compared to the single-parameter classification and the AdaBoost methods based on the traditional average and Bagging method, the PCAD method presents higher correct rates in classification. Finally, the proposed method is applied to landmine detection. The results demonstrate that the landmine is a composite scatterer that can generate surface scattering signals on its surface and dipole and volume scattering signals from its interior; based on the color-coded two-dimensional image by PCAD, we can distinguish landmines from other targets.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Earth Observation Semantic Data Mining: Latent Dirichlet Allocation-Based
           Approach

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      Authors: Reza Mohammadi Asiyabi;Mihai Datcu;
      Pages: 2607 - 2620
      Abstract: Recent advances in remote sensing technology have provided (very) high spatial resolution Earth Observation data with abundant latent semantic information. Conventional data processing algorithms are not capable of extracting the latent semantic information form these data and harness their full potential. As a result, semantic information discovery methods, based on data mining techniques, such as latent Dirichlet allocation and bag of visual words models, can discover the latent information. Despite their crucial rule, there are only a few studies in the field of semantic data mining for remote sensing applications. This article is focused on this shortage. Three different scenarios are used to evaluate the semantic information discovery in various remote sensing applications, including both optical and synthetic aperture radar (SAR) data with different spatial resolutions. In the first scenario, semantic discovery method correlated the semantic perception of the user and machine to correct and enhance the user defined Ground Truth map in very high-resolution RGB data. The potential of the semantic discovery is evaluated for wildfire affected area detection in Sentinel-2 data in the second scenario. Finally, in the third scenario, the semantic discovery method is utilized to detect the misclassifications as well as the patches with ambiguous or multiple semantic labels in a Sentinel-1 SAR patch-based benchmark dataset to enhance the robustness and accuracy of the annotation in the dataset. Our results in these three scenarios demonstrated the capability of the data-mining-based semantic information discovery methods for various remote sensing.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Novel Consistency Calibration Method for DMSP-OLS Nighttime Stable Light
           Time-Series Images

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      Authors: Liu Yang;Jingjing Cao;Li Zhuo;Qingli Shi;
      Pages: 2621 - 2631
      Abstract: The Defense Meteorological Satellite Program-Operational Line-Scan System (DMSP-OLS) nighttime stable light (NSL) products have been widely employed for studying human activities and urbanization. However, due to the lack of on-board calibration of the OLS sensors, narrow radiation range, and different satellite platforms, the NSL products suffer from several limitations such as saturation effect and inconsistency problem. To solve these issues, this study proposed a spatio-temporal adaptive pseudo-invariant pixel (STAPIP) scheme for NSL time-series consistency calibration based on eight-year DMSP-OLS radiance calibrated (RC) products. The unsaturated pixels were calibrated with the temporal adaptive pseudo-invariant pixel (TAPIP) method, while the saturated pixels were calibrated with the spatial adaptive PIP (SAPIP) method. To verify the proposed method's effectiveness, we applied it in China and compared its results with the results of several existing NSL calibration methods. Then we further analyzed the correlations between the calibrated NSL time-series images and several important socio-economic indicators, such as population, gross domestic product (GDP), and electricity consumption (EC). Results showed that the NSL time-series images calibrated by the STAPIP method have the best consistency and highest correlation with socio-economic indicators at both provincial and city levels. Thus, this study can provide an accurate and stable NSL time-series product for research on human activities and the urbanization process.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • DAT-CNN: Dual Attention Temporal CNN for Time-Resolving Sentinel-3
           Vegetation Indices

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      Authors: Damian Ibañez;Ruben Fernandez-Beltran;Filiberto Pla;Naoto Yokoya;
      Pages: 2632 - 2643
      Abstract: The synergies between Sentinel-3 (S3) and the forthcoming fluorescence explorer (FLEX) mission bring us the opportunity of using S3 vegetation indices (VI) as proxies of the solar-induced chlorophyll fluorescence (SIF) that will be captured by FLEX. However, the highly dynamic nature of SIF demands a very temporally accurate monitoring of S3 VIs to become reliable proxies. In this scenario, this article proposes a novel temporal reconstruction convolutional neural network (CNN), named dual attention temporal CNN (DAT-CNN), which has been specially designed for time-resolving S3 VIs using S2 and S3 multitemporal observations. In contrast to other existing techniques, DAT-CNN implements two different branches for processing and fusing S2 and S3 multimodal data, while further exploiting intersensor synergies. Besides, DAT-CNN also incorporates a new spatial–spectral and temporal attention module to suppress uninformative spatial–spectral features, while focusing on the most relevant temporal stamps for each particular prediction. The experimental comparison, including several temporal reconstruction methods and multiple operational Sentinel data products, demonstrates the competitive advantages of the proposed model with respect to the state of the art. The codes of this article will be available at https://github.com/ibanezfd/DATCNN.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Feature-Based Constraint Deep CNN Method for Mapping Rainfall-Induced
           Landslides in Remote Regions With Mountainous Terrain: An Application to
           Brazil

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      Authors: Guosen Xu;Yi Wang;Lizhe Wang;Lucas Pedrosa Soares;Carlos H. Grohmann;
      Pages: 2644 - 2659
      Abstract: Landslides have caused tremendous damage to human lives and property safety. However, the complex environment of mountain landslides and the vegetation coverage around landslides make it difficult to identify landslides quickly and efficiently using high-resolution images. To address this challenge, this article presents a feature-based constraint deep U-Net (FCDU-Net) method to detect rainfall-induced mountainous landslides. Usually, the vegetation in the landslide area is severely damaged, and the vegetation coverage can indirectly reflect the spatial extent of the landslide. Meanwhile, the texture features of high-resolution images can characterize the surface environment of landslide hazards to a certain extent. We first introduce auxiliary features of normalized difference vegetation index and gray-level co-occurrence matrix into the proposed method to further improve the detection performance. Then, to minimize the information redundancy of these features and the image, we combine Relief-F and Deep U-Net to screen the optimal features to effectively identify accurate and detailed landslide boundaries. Compared with traditional semantic segmentation methods, the FCDU-Net method can capture fine-grained details in high-resolution images and produce more accurate segmentation results. We conducted experiments by applying the proposed method and other most popular semantic segmentation methods to a high-resolution RapidEye image in Rio de Janeiro, Brazil. The results demonstrate that the FCDU-Net method can achieve better landslide detection results than the other semantic segmentation methods, and the evaluation measures of Precision, F1 score, and mean Intersection-over-Union are as high as 88.87%, 81.17%, and 83.19%, respectively. Furthermore, we quantitatively analyze the effect of the convolution input window size on the performance of FCDU-Net in detecting landslides. We believe that FCDU-Net can serv- as a reliable tool for fast and accurate regional landslide hazard surveys.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • High-Resolution Radar Sensing Sea Surface States During AMK-82 Cruise

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      Authors: Alexey Ermoshkin;Alexander Molkov;
      Pages: 2660 - 2666
      Abstract: Every year situation when theArctic seas are free of ice is becoming more frequent. It allows scientists to study hard-to-reach areas using well-equipped research vessels instead of icebreakers. During the COVID-19 pandemic, the successful expedition of the research vessel “Academician Mstislav Keldysh” with more than 60 scientists from 15 countries across the four Arctic seas (Barents, Kara, Laptev, and East Siberian) on September–November 2020 seems like a real wonder. One of the expedition tasks was remote sensing of different hydrophysical processes by their manifestation on the sea surface using marine radar. This article proposes the method of generating high spatial resolution radar maps of the sea surface and algorithms of hydrophysical processes identification. This article also presents examples of registered processes, such as wind waves, ice fields with different types of ice (grease ice, pancake ice, nilas, and young ice), manifestations of internal waves observed in the Kara Gate and Vilkitsky Strait, as well as manifestations of intense methane seeps on the sea surface. This article contains quantitative estimations of the physical parameters of the observed processes underlying the effectiveness of Doppler marine radars in harsh conditions of the Arctic seas.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Change Detection From Synthetic Aperture Radar Images via Dual Path
           Denoising Network

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      Authors: Junjie Wang;Feng Gao;Junyu Dong;Qian Du;Heng-Chao Li;
      Pages: 2667 - 2680
      Abstract: Benefited from the rapid and sustainable development of synthetic aperture radar (SAR) sensors, change detection from SAR images has received increasing attentions over the past few years. Existing unsupervised deep learning-based methods have made great efforts to exploit robust feature representations, but they consume much time to optimize parameters. Besides, these methods use clustering to obtain pseudolabels for training, and the pseudolabeled samples often involve errors, which can be considered as “label noise.” To address these issues, we propose a dual path denoising network (DPDNet) for SAR image change detection. In particular, we introduce the random label propagation to clean the label noise involved in preclassification. We also propose the distinctive patch convolution for feature representation learning to reduce the time consumption. Specifically, the attention mechanism is used to select distinctive pixels in the feature maps, and patches around these pixels are selected as convolution kernels. Consequently, the DPDNet does not require a great number of training samples for parameter optimization, and its computational efficiency is greatly enhanced. Extensive experiments have been conducted on five SAR datasets to verify the proposed DPDNet. The experimental results demonstrate that our method outperforms several state-of-the-art methods in change detection results.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace
           Clustering for Hyperspectral Image Classification

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      Authors: Hongliang Lu;Hongjun Su;Jun Hu;Qian Du;
      Pages: 2681 - 2695
      Abstract: Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this article, two CR-based dynamic ensemble selection (DES) methods using multiview kernel collaborative subspace clustering (MVKCSC) and random subspace MVKCSC (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multiview learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multifeature information. Moreover, a new method of constructing the Laplacian matrix using kernel CR coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase the diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Estimating Water Content and Grain Size of Intertidal Flat Sediments Using
           Visible to Shortwave-Infrared Reflectance and Sentinel 2A Data: A Case
           Study of the Red River Delta, Vietnam

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      Authors: Vu Thi Thu Thuy;Nguyen Thi Thu Ha;Katsuaki Koike;Nguyen Thien Phuong Thao;Pham Ngoc Trung;Dinh Xuan Thanh;
      Pages: 2696 - 2708
      Abstract: Sediment properties such as water content (WC) and grain size (GS) are essential to characterize the environmental conditions of tidal flats. This article aimed to develop appropriate models to estimate the WC and GS of surface sediments for an intertidal flat on the Red river delta (Vietnam) using Sentinel 2A (S2A) images. The spectral reflectance, WC, and GS of 96 sub-samples from 12 sediment samples collected on December 17, 2017 were measured to clarify their relationships. The WC was highly correlated with the reflectance ratio of two shortwave-infrared bands, R(2190)/R(1610) (R2 = 0.93). The median GS (D50) at 0%, 15%, and 20% of WC was significantly correlated with the reflectance ratio of the near-infrared band (842 nm) versus the visible-green band (560 nm) (R2> 0.78). Next, D50 was estimated from a multivariate regression model using this band ratio, the visible-red band (665 nm), and WC. The accuracy of the models was verified by comparisons with WC and D50 from 20 samples collected on March 12th 2019 (RMSE of both WC and D50 < 15%). Then, the WC and sediment type distributions were mapped by applying these models to two S2A scenes. The maps showed high WC (>30%) in very fine sediments (silts), which is consistent with other intertidal flats with similar sediment types. This article was limited to fine sediment samples. Therefore, our next step is to incorporate coarse sediments into the models to provide more universal mapping of WC and sediment types.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • On the Use of Ocean Surface Doppler Velocity for Oceanic Front Extraction
           From Chinese Gaofen-3 SAR Data

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      Authors: Kai Sun;Jinsong Chong;Lijie Diao;Zongze Li;Xianen Wei;
      Pages: 2709 - 2720
      Abstract: Oceanic front extraction from synthetic aperture radar (SAR) images is of great significance to the study of marine ecosystems. At present, the methods for oceanic front extraction are usually based on SAR intensity images, which treat oceanic fronts as edge-like features in the SAR images. However, affected by radar parameters and sea state, sometimes, oceanic front signatures may not be clearly visible in the SAR intensity images. Therefore, existing methods are limited. In order to solve this problem, a method combining intensity and Doppler information for oceanic front extraction is proposed. Using Chinese Gaofen-3 single-look complex (SLC) data, three cases where oceanic front signatures are clearly visible, partially visible, and extremely weak in the SAR intensity images are investigated, which demonstrate how the Doppler velocity gradient across a front can be leveraged to enhance the extraction of oceanic fronts from SAR data. The results show that the Doppler data, as supplementary information, not only can complement the oceanic fronts extracted from SAR intensity images, but also can be used as a reference for the oceanic fronts extracted from SAR intensity images.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Learning Calibrated-Guidance for Object Detection in Aerial Images

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      Authors: Zongqi Wei;Dong Liang;Dong Zhang;Liyan Zhang;Qixiang Geng;Mingqiang Wei;Huiyu Zhou;
      Pages: 2721 - 2733
      Abstract: Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art approaches tend to develop elaborate attention mechanisms for the space-time feature calibrations with arduous computational complexity, while surprisingly ignoring the importance of feature calibrations in channel-wise. In this work, we propose a simple yet effective calibrated-guidance (CG) scheme to enhance channel communications in a feature transformer fashion, which can adaptively determine the calibration weights for each channel based on the global feature affinity correlations. Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance. Then, rerepresenting each channel by aggregating all the channels weighted together via the guidance operation. Our CG is a general module that can be plugged into any deep neural networks, which is named as CG-Net. To demonstrate its effectiveness and efficiency, extensive experiments are carried out on both oriented object detection task and horizontal object detection task in aerial images. Experimental results on two challenging benchmarks (i.e., DOTA and HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art performance in accuracy with a fair computational overhead. The source code has been open sourced at https://github.com/WeiZongqi/CG-Net.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Mapping Urban Impervious Surface With an Unsupervised Approach Using
           Interferometric Coherence of SAR Images

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      Authors: Xindan Liang;Yinyi Lin;Hongsheng Zhang;
      Pages: 2734 - 2744
      Abstract: Impervious surface is significant in hydrology, urban management, ecology, and other research areas. Therefore, extracting impervious surface is crucial to understanding the change of environment and ecosystem. However, previous supervised classification methods usually rely on comprehensive training samples and human experiences. The article on automatic and efficient impervious surface extraction is still underexplored. In this study, we investigated the potential of using interferometric synthetic aperture radar technology for unsupervised urban impervious surface (UIS) mapping. A total 136 coherence maps of Hong Kong with different perpendicular and temporal baselines were used to classify UIS and non-UIS through setting different coherence thresholds. We proposed a new method, entitled interferometric coherence thresholding method, which can achieve high classification accuracy using coherence map. The result illustrates: first, using a threshold of 0.4, nearly 90% of images achieve an overall accuracy of over 80%. The highest one reaches 88.25%, which is much higher than using K-means and ISODATA method; second, small temporal baselines (12 and 24 days) are likely to reduce the classification accuracy; third, using optimal classification threshold, the coherence of two SAR images would not bring large impact on classification results.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Spectral-Spatial Feature Extraction Method With Polydirectional CNN for
           Multispectral Image Compression

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      Authors: Fanqiang Kong;Kedi Hu;Yunsong Li;Dan Li;Xin Liu;Tariq S. Durrani;
      Pages: 2745 - 2758
      Abstract: Convolutional neural networks (CNN) has been widely used in the research of multispectral image compression, but they still face the challenge of extracting spectral feature effectively while preserving spatial feature with integrity. In this article, a novel spectral-spatial feature extraction method is proposed with polydirectional CNN (SSPC) for multispectral image compression. First, the feature extraction network is divided into three parallel modules. The spectral module is employed to obtain spectral features along the spectral direction independently, and simultaneously, with two spatial modules extracting spatial features along two different spatial directions. Then all the features are fused together, followed by downsampling to reduce the size of the feature maps. To control the tradeoff between the rate loss and the distortion, the rate-distortion optimizer is added to the network. In addition, quantization and entropy encoding are applied in turn, converting the data into bit stream. The decoder is structurally symmetric to the encoder, which is convenient for structuring the framework to recover the image. For comparison, SSPC is tested along with JPEG2000 and three-dimensional (3-D) SPIHT on the multispectral datasets of Landsat-8 and WorldView-3 satellites. Besides, to further validate the effectiveness of polydirectional CNN, SSPC is also compared with a normal CNN-based algorithm. The experimental results show that SSPC outperforms other methods at the same bit rates, which demonstrates the validity of the spectral-spatial feature extraction method with polydirectional CNN.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Polarimetric SAR Image Classification Based on Ensemble Dual-Branch CNN
           and Superpixel Algorithm

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      Authors: Wenqiang Hua;Cong Zhang;Wen Xie;Xiaomin Jin;
      Pages: 2759 - 2772
      Abstract: Recently, convolutional neural networks (CNNs) have been successfully utilized in polarimetric synthetic aperture radar (PolSAR) image classification and obtained promising results. However, most CNN-based classification methods require a large number of labeled samples and it is difficult to obtain sufficient labeled samples. For this reason, an ensemble dual-branch CNN (EDb-CNN) is proposed for PolSAR image classification with small samples. First, to solve the problem of the small sample in PolSAR image classification, a new data enhancement method based on the superpixel algorithm is proposed to expand the number of labeled samples. Second, to obtain different scales of features from PolSAR images, a Db-CNN model is proposed. This model contains two parallel CNN structures. One CNN branch is used to extract the polarization features from the complex coherency matrix. The other branch is utilized to extract the spatial features based on weighted spatial neighborhood. On the top of these two branches, a feature fusion model is adopted to combine these two deep features, and a weighted loss function is employed to improve the learning procedure. Then, the ensemble learning algorithm is used for each CNN branch and Db-CNN network to obtain the better classification results. Finally, a postprocess algorithm based on the superpixel algorithm is proposed to improve the consistency of classification results. Experiments on two PolSAR datasets show that the proposed method achieves a much better performance than other classification methods, especially when only a few labeled samples are available.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Expand Dimensional of Seismic Data and Random Noise Attenuation Using
           Low-Rank Estimation

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      Authors: Javad Mafakheri;Amin Roshandel Kahoo;Rasoul Anvari;Mokhtar Mohammadi;Mohammad Radad;Mehrdad Soleimani Monfared;
      Pages: 2773 - 2781
      Abstract: Random noise attenuation in seismic data requires employing leading-edge methods to attain reliable denoised data. Efficient noise removal, effective signal preservation and recovery, reasonable processing time with a minimum signal distortion and seismic event deterioration are properties of a desired noise suppression algorithm. There are various noise attenuation methods available that more or less have these properties. We aim to obtain more effective denoised seismic data by assuming 3-D seismic data as a tensor in order three and increasing its dimension to 4-D seismic data by employing continuous wavelet transform (CWT). First, we map 3-D block seismic data to smaller blocks to estimate the low-rank component. The CWT of the tensor is calculated along the third dimension to extract the singular values and their related left/right vectors in the wavelet domain. Afterward, the effective low-rank component is extracted using optimized coefficients for each singular value. Thresholding is applied in the wavelet domain along the third dimension to calculate effective coefficients. Two synthetic and field data examples are considered for performance evaluation of the proposed method, and the results were compared with the competitive random noise suppression methods, such as the tensor optimum shrinkage singular value decomposition, the iterative block tensor singular value thresholding, and the block matching 4-D algorithms. Qualitative and quantitative comparison of the proposed method with other methods indicates that the proposed method efficiently eliminates random noise from seismic data.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • AlgaeNet: A Deep-Learning Framework to Detect Floating Green Algae From
           Optical and SAR Imagery

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      Authors: Le Gao;Xiaofeng Li;Fanzhou Kong;Rencheng Yu;Yuan Guo;Yibin Ren;
      Pages: 2782 - 2796
      Abstract: This article developed a scalable deep-learning model, the AlgaeNet model, for floating Ulva prolifera (U. prolifera) detection in moderate resolution imaging spectroradiometer (MODIS) and synthetic aperture radar (SAR) images. We labeled 1055/4071 pairs of samples, among which 70%/30% were used for training/validation. As a result, the model reached an accuracy of 97.03%/99.83% and a mean intersection over union of 48.57%/88.43% for the MODIS/SAR images. The model was designed based on the classic U-Net model with two tailored modifications. First, the physics information input was a multichannel multisource remote sensing data. Second, a new loss function was developed to resolve the class-unbalanced samples (algae and seawater) and improve model performance. In addition, this model is expandable to process images from optical sensors (e.g., MODIS/GOCI/Landsat) and SAR (e.g., Sentinel-1/GF-3/Radarsat-1 or 2), reducing the potential biases due to the selection of extraction thresholds during the traditional threshold-based segmentation. We process satellite images containing U. prolifera in the Yellow Sea and draw two conclusions. First, adding the 10-m high-resolution SAR imagery shows a 63.66% increase in algae detection based on the 250-m resolution MODIS image alone. Second, we define a floating and submerged ratio number (FS ratio) based on the floating and submerged parts of U. prolifera detected by SAR and MODIS. A research vessel measurement confirms the FS ratio to be a good indicator for representing different life phases of U. prolifera.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Wave Height Estimation and Validation Based on the UFS Mode Data of
           Gaofen-3 in South China Sea

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      Authors: Limin Cui;Mingsen Lin;Youguang Zhang;Yongjun Jia;
      Pages: 2797 - 2804
      Abstract: The ultrafinestrip (UFS) of the Gaofen-3 (GF-3) satellite provides extensive ocean wave details due to the high spatial resolution of 3 m. In this article, a new empirical model is developed to estimate wave height from GF-3 UFS mode data in South China Sea (SCS). Traditional methods either have more input parameters or complex forms or complex transformations, and most of them depend on the visible wave pattern. The model has the advantages of fewer input parameters and unnecessary visual wave patterns. The wave height estimation model is developed based on the collated data pairs between GF-3 UFS data in horizontal-horizontal polarization and the ERA5 reanalysis dataset from 2018 to 2021. The datasets are randomly divided into two groups. One group (about 80%) is used to develop the empirical relation; the other group (about 20%) and some altimeter data are used for significant wave height (SWH) verification. The comparison of the model data with the remaining ERA5 (ECMWF Reanalysis v5) data and the altimeter shows that the root-mean-square error is 0.41 and 0.47 m, the scatter index is 29.24% and 29.79%, and the correlation coefficient is 0.90 and 0.82, respectively. These statistical indices suggest that the developed model is suitable for SWH retrievals. So, the study results indicate that the GF-3 UFS mode data provide valuable information about wave conditions in the SCS.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • CGC-NET: Aircraft Detection in Remote Sensing Images Based on Lightweight
           Convolutional Neural Network

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      Authors: Ting Wang;Xiaodong Zeng;Changqing Cao;Wei Li;Zhejun Feng;Jin Wu;Xu Yan;Zengyan Wu;
      Pages: 2805 - 2815
      Abstract: In the past few years, aircraft detection in remote sensing (RS) images has been an important research hotspot, and it is very crucial in plenty of military applications. Based on the high computational cost of the model and numerous parameters, deep convolution neural networks-based algorithms have excellent performance in the aircraft detection task. However, it is still difficult to detect aircraft due to the complex background of RS images, various types of aircraft, and so on. In addition, it is difficult and costly to make labels for satellite-based optical RS images. Consequently, we propose an end-to-end lightweight aircraft detection framework called CGC-NET (a network based on circle grayscale characteristics), which can accurately detect aircraft with a few training samples. There are only a small number of trainable parameters in CGC-NET, which greatly reduces the need for large datasets. Extensive evaluations indicate the excellent performance of CGC-NET, in which the F-score can reach 91.06% and the model size is only 0.88 M. Therefore, CGC-NET can be used to accurately detect aircraft targets simply and effectively.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Unsupervised Balanced Hash Codes Learning With Multichannel Feature Fusion

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      Authors: Yaxiong Chen;Dongjie Zhao;Xiongbo Lu;Shengwu Xiong;Huangting Wang;
      Pages: 2816 - 2825
      Abstract: Unsupervised hashingalgorithms are widely used in large-scale remote sensing images (RSIs) retrieval task. However, existing RSI retrieval algorithms fail to capture the multichannel characteristic of multispectral RSIs and the balanced property of hash codes, which lead the poor performance of RSI retrieval. To tackle these issues, we develop an unsupervised hashing algorithm, namely, variational autoencoder balanced hashing (VABH), to leverage multichannel feature fusion and multiscale context information to perform RSI retrieval task. First, multichannel feature fusion module is designed to extract RSI feature information by leveraging the multichannel properties of multispectral RSI. Second, multiscale learning module is developed to learn the multiscale context information of RSIs. Finally, a novel objective function is designed to capture the discrimination and balanced property of hash codes in the hashing learning process. Comprehensive experiments on diverse benchmark have well demonstrated the reasonableness and effectiveness of the proposed VABH algorithm.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • Assimilation of SBAS-InSAR Based Vertical Deformation Into Land Surface
           Model to Improve the Estimation of Terrestrial Water Storage

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      Authors: Kun Chen;Guoxiang Liu;Wei Xiang;Tao Sun;Kun Qian;Jiaxin Cai;Saied Pirasteh;Xiao Chen;
      Pages: 2826 - 2835
      Abstract: The gravity recovery and climate experiment (GRACE) provides an unprecedented opportunity to detect the spatial and temporal variation of the terrestrial water storage (TWS) for regional to continental scales. However, the GRACE system's coarse temporal resolution (∼monthly) and data discontinuity missing perplexed the TWS research during the operation. In this article, the data assimilation (DA) method was employed to integrate the vertical deformation obtained from the small baseline subset (SBAS) InSAR processing into the NASA catchment land surface model (CLSM), which improved the estimation of the TWS. First, we used a one-dimensional ensemble Kalman filter for DA research to estimate the TWS in Dali Prefecture, southwestern China. Finally, we compared the estimated TWS with the GRACE-based TWS from December 2, 2018 to January 21, 2021. The unbiased root-mean-square of the open loop (OL; without DA) method and the SBAS-InSAR DA method are 61 mm and 30 mm in Dali Prefecture, respectively. Results revealed that the numerical difference between the estimated TWS and the GRACE TWS retrievals was significantly decreased by the SBAS-InSAR DA method than the OL method. In addition, the temporal resolution of the SBAS-InSAR DA-based TWS was improved to 12 days compared with GRACE-based TWS. Furthermore, we recovered the discontinuous deletion and blank of GRACE-based TWS from 2015 to 2018 by the SBAS-InSAR DA method.
      PubDate: 2022
      Issue No: Vol. 15 (2022)
       
  • A Theoretical Analysis of Granulometry-Based Roughness Measures on
           Cartosat DEMs

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