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  Subjects -> ELECTRONICS (Total: 188 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 7)
Advances in Electronics     Open Access   (Followers: 94)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Power Electronics     Open Access   (Followers: 39)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 344)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 26)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 14)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 30)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 21)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 13)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 304)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 1)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 123)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 103)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 102)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 55)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 207)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 100)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 81)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 51)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 75)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 73)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 58)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 44)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 78)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access  
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 57)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 74)
Industrial Technology Research Journal Phranakhon Rajabhat University     Open Access  
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 38)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 11)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 11)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 35)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 176)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 42)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 56)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 78)
Solid State Electronics Letters     Open Access  
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Transactions on Electrical and Electronic Materials     Hybrid Journal  
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Ural Radio Engineering Journal     Open Access  
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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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: 56  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • IEEE GEOSCIENCE AND REMOTE SENSING SOCIETY
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Institutional listings
    • Abstract: Advertisements.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent
           Semantic Analysis for Hyperspectral Unmixing
    • Authors: José Antonio Gallardo Jaramago;Mercedes Eugenia Paoletti;Juan Mario Haut;Ruben Fernandez-Beltran;Antonio Plaza;Javier Plaza;
      Pages: 3156 - 3167
      Abstract: Hyperspectral unmixing (HU) is an important task for remotely sensed hyperspectral (HS) data exploitation. It comprises the identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube. Several methods have been developed for (semi-) supervised and automatic identification of endmembers and abundances. Recently, the statistical dual-depth sparse probabilistic latent semantic analysis (DEpLSA) method has been developed to tackle the HU problem as a latent topic-based approach in which both endmembers and abundances can be simultaneously estimated according to the semantics encapsulated by the latent topic space. However, statistical models usually lead to computationally demanding algorithms and the computational time of the DEpLSA is often too high for practical use, in particular, when the dimensionality of the HS data cube is large. In order to mitigate this limitation, this article resorts to graphical processing units (GPUs) to provide a new parallel version of the DEpLSA, developed using the NVidia compute device unified architecture. Our experimental results, conducted using four well-known HS datasets and two different GPU architectures (GTX 1080 and Tesla P100), show that our parallel versions of the DEpLSA and the traditional pLSA approach can provide accurate HU results fast enough for practical use, accelerating the corresponding serial versions in at least 30x in the GTX 1080 and up to 147x in the Tesla P100 GPU, which are quite significant acceleration factors that increase with the image size, thus allowing for the possibility of the fast processing of massive HS data repositories.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Recognizing Global Reservoirs From Landsat 8 Images: A Deep Learning
           Approach
    • Authors: Weizhen Fang;Cunguang Wang;Xi Chen;Wei Wan;Huan Li;Siyu Zhu;Yu Fang;Baojian Liu;Yang Hong;
      Pages: 3168 - 3177
      Abstract: Man-made reservoirs are key components of terrestrial hydrological systems. Identifying the location and number of reservoirs is the premise for studying the impact of human activities on water resources and environmental changes. While complete bottom-up censuses can provide a comprehensive view of the reservoir landscape, they are time-consuming and laborious and are thus infeasible on a global scale. Moreover, it is challenging to distinguish man-made reservoirs from natural lakes in remote sensing images. This study proposes a convolutional neural network (CNN)-based framework to recognize global reservoirs from Landsat 8 imageries. On the basis of the HydroLAKES dataset, a Landsat 8 cloud-free mosaic of 2017 was clipped for each feature (reservoir or lake) and was resized into 224 × 224 patches, which were collected as training and testing samples. Compared to other deep learning methods (Alexnet and VGG) and state-of-the-art traditional machine learning methods (support vector machine, random forest, gradient boosting, and bag-of-visual-words), we found that fine-tuning the pretrained CNN model, ResNet-50, could reach the highest accuracy (91.45%). Application cases in Kansas (USA, North America), Mpumalanga (South Africa, Africa), and Kostanay (Kazakhstan, Asia) resulted in classification accuracies of better than 99%, which showed the applicability of the proposed ResNet-50 model to the extraction of reservoirs from a vast amount of moderate resolution images. The framework that was developed in this paper is the first attempt to combine remote sensing big data and the deep learning technique to the recognition of reservoirs at a global scale.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Estimation of Layer-Averaged Rain Rate From Zenith Pointing Ka-Band Radar
           Measurements Using Attenuation Method
    • Authors: Subrata Kumar Das;Yogesh Kisan Kolte;Uriya Veerendra Murali Krishna;Sachin Madhukar Deshpande;Abhishek Kumar Jha;Govindan Pandithurai;
      Pages: 3178 - 3183
      Abstract: Traditionally, Ka-band radars are opted for cloud studies as the radar signal at this frequency gets attenuated during rainy condition. In this work, an attenuation-based method is used to estimate the layer-averaged rain rate from vertically pointing Ka-band radar deployed at Mandhardev (18.04 °N, 73.87 °E, ~1.3 km AMSL), a remote station in the hill-top of Western Ghats, India. At Ka-band, the rain rate retrieval depends on the linear relation between specific attenuation coefficient and rain rate. The specific attenuation coefficient is calculated from T-matrix scattering simulation performed at Ka-band frequency using the Joss-Waldvogel disdrometer (JWD) raindrop data. The radar retrieved rain rates for 0.5- and 1-km rain layer depths are evaluated with the collocated JWD rain rate measurements. The rain rates retrieved from 1-km rain layer depth shows higher correlation, 0.75, and small bias, 2.36 mm h-1, compared to 0.72 and 3.65 mm h-1, respectively, for 0.5-km rain layer depth retrievals. Further, the vertical profiles of rain rate are constructed for both 0.5- and 1-km rain layer depths. It is observed that the retrieval error decreases, with an increase in rain rates. The good comparison of the rain rate between the radar and the JWD validates the possibility of using cloud radar observations for the retrieval of the rain rate.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • An L-Band Brightness Temperature Disaggregation Method Using S-Band
           Radiometer Data for the Water Cycle Observation Mission (WCOM)
    • Authors: Panpan Yao;Jiancheng Shi;Tianjie Zhao;Michael H. Cosh;Rajat Bindlish;Hui Lu;
      Pages: 3184 - 3193
      Abstract: The water cycle observation mission (WCOM) will build upon previous L and C band passive microwave soil moisture satellite missions. WCOM will consist of a passive microwave synthetic aperture radiometer operating at L, S, and C bands. The WCOM requirements for passive soil moisture are to estimate soil moisture in the top 5 cm of soil layer with an error less than 0.04 m3/m3, at 15-km resolution and with a 3-day revisit. A new set of algorithms for these multi-frequency platforms will need to be developed for estimating the data products at the desired resolution. To accomplish this, a brightness temperature (TB) downscaling methodology is developed that uses passive S-band TB (30 km) to downscale L-band TB (50 km) and to estimate soil moisture at a 30-km resolution, based on the linear relationships between the passive signals of L band and S band. To test this downscaling method, analysis was performed using PALS data from the Soil Moisture Experiments in 2002 (SMEX02). For this study, 4-km L-band observations were downscaled to 800 m. The root mean square errors between the downscaled TBL at 800 m with the observed TBL at 800 m are 2.63 and 1.60 K for H and V polarizations, respectively. The results also showed that it was possible to use these disaggregated TB to estimate soil moisture to meet the mission requirement of 0.04 m3/m3. These results showed that we can obtain higher resolution soil moisture from the L-band passive TB with a high accuracy (
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Predicting Radiometric Effects of a Rough Sea Surface, Whitecaps, Foam,
           and Spray Using SURFER 2D
    • Authors: Derek M. Burrage;Magdalena D. Anguelova;David W. Wang;Joel C. Wesson;
      Pages: 3194 - 3207
      Abstract: Whitecap (WC) formation due to waves breaking on a wind-roughened sea surface facilitates the exchange of mass, momentum, and energy across the air-sea interface. While approximate analytical electromagnetic (EM) models paired with surface gravity wave spectra have been used to predict surface roughness emissivity enhancement, these methods do not reveal details of the response to foam, breaking wave, and WC geometry. They also ignore possible coupling between the roughness and WC emissivity effects. We report the application of a full-wave finite-difference time-domain (FDTD) EM model to investigate the separate and combined emissivity effects of specific surface roughness profiles, associated WC fields, and overlying spray. The model solves Maxwell's equations directly for an arbitrary free space and dielectric configuration. It is applied to multiple dielectric layers representing foam and spray overlying flat and rough sea surfaces. The foam layer profiles are adapted from Anguelova's L-band radiative transfer model and the rough surface is a statistical realization of the Kudryavtsev gravity wave spectrum. The model is also used to investigate the secondary effect of sea state on emissivity for a given mean square slope, which is the primary factor governing rough surface emissivity enhancement. The accuracy and precision of the FDTD model emissivity estimates and the detectability of WCs using L-band radiometry are assessed under various wind conditions, including those of tropical storm and category 1 hurricane strength. The prospects for performing Monte Carlo simulations for stronger winds and deterministic simulations of breakers with WCs of various void fractions, shapes, and scales are also considered.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Severe Marine Weather Systems During Freeze-Up in the Chukchi Sea:
           Cold-Air Outbreak and Mesocyclone Case Studies From Satellite Multisensor
           Measurements and Reanalysis Datasets
    • Authors: Mikhail K. Pichugin;Irina A. Gurvich;Elizaveta V. Zabolotskikh;
      Pages: 3208 - 3218
      Abstract: In the present article, cold-air outbreak (CAO) and polar low (PL) events were examined over the Chukchi Sea during low sea ice extent of the basin in the November-December period, 2015. An analysis of weather events was performed using multisensor satellite measurements, the NCEP Climate Forecast System, version 2 (CFSv2), and Arctic System Reanalysis, version 2 (ASR2). The CFSv2 data showed that the most intense cold advection occurred at the 1000-900 hPa boundary layer. It was shown that CAO had been developing from November 30 to December 6 and was accompanied by the sea surface wind speed exceeding 20 m/s and rapid formation and drift of sea ice. Analysis of the sea ice response to the CAO showed fast freezing of the Chukchi Sea for seven days indicating excessive heat loss of the water basin. In addition, significant increase in the open and very open ice areas was observed. These areas could be exposed to excessive cold atmosphere, releasing heat into the atmosphere. PL events have been identified during intensive mesocyclogenesis over the sea during 15-17 November. Weather conditions were characterized by the values of wind speed reaching 15-17 m/s and dry air with total water vapor content (5-7 kg/m2). Analysis of the synthetic aperture radar image revealed the position and horizontal size of the meso-α-cyclone, the fine structure of the mesoscale frontal system of the PL forming meso-γ-vortices, and the wave-like line of the horizontal wind shear in the local convergence zone. The representation of PL events in sea level pressure and near-surface wind speed fields from the ASR2 and CFSv2 datasets was ambiguous. However, the satellite-based and CFSv2 wind speeds better agreed for high-wind conditions.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing
           Images
    • Authors: Pourya Shamsolmoali;Masoumeh Zareapoor;Ruili Wang;Huiyu Zhou;Jie Yang;
      Pages: 3219 - 3232
      Abstract: Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sea-land segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and land. Although several convolutional neural networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixel-wise sea-land segmentation, a residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each downsampling and upsampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multiscale contextual information. Each dense network block contains multilevel convolution layers, short-range connections, and an identity mapping connection, which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results while minimizing computational costs. We have performed extensive experiments on two real datasets, Google-Earth and ISPRS, and compared the proposed RDU-Net against several variations of dense networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Comparison of Satellite Passive Microwave With Modeled Snow Water
           Equivalent Estimates in the Red River of the North Basin
    • Authors: Ronny Schroeder;Jennifer M. Jacobs;Eunsang Cho;Carrie M. Olheiser;Michael M. DeWeese;Brian A. Connelly;Michael H. Cosh;Xinhua Jia;Carrie M. Vuyovich;Samuel E. Tuttle;
      Pages: 3233 - 3246
      Abstract: The Red River of the North basin (RRB) is vulnerable to spring snowmelt flooding because of its flat terrain, low permeability soils, and the presence of river ice jams resulting from the river's northward flow direction. The onset and magnitude of major flood events in the RRB have been very difficult to forecast, in part due to limited field observations of snow water equivalent (SWE). Coarse-resolution (25-km) passive microwave observations from satellite instruments are well suited for the monitoring of SWE. Despite routine use in the Earth sciences community to document the impacts of climate change, the use of passive microwave observations in operational flood forecasting is rare. This paper compares daily satellite passive microwave SWE observations from special sensor microwave/imager (SSM/I) and special sensor microwave imager/sounder (SSMIS), advanced microwave scanning radiometer for earth observing system (AMSR-E), and advanced microwave scanning radiometer 2 (AMSR2) from 2003 to 2016 to modeled output from the SNOw Data Assimilation System (SNODAS) and Global Snow Monitoring for Climate Research-2 (GlobSnow-2) in the RRB to determine the differences between the remotely sensed SWE estimates and the model products currently used in flood forecasting. Results show statistically significant differences between the satellite observations and SNODAS in the northern region of the basin that were not evident in the southern region. Satellite estimates of peak SWE in the forecast subbasins in the northern region were 40-125% higher than the model results which points to the lack of ground observations used to constrain the model simulations. This paper recommends that satellite SWE observations should be considered for improving operational snowmelt forecasting in the RRB.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Investigating the Relationship Between Satellite-Based Freeze/Thaw
           Products and Land Surface Temperature
    • Authors: Jeremy Johnston;Viviana Maggioni;Paul Houser;
      Pages: 3247 - 3271
      Abstract: This paper investigates surface temperature variables as they relate to passive microwave-derived surface freeze/thaw (FT) state and assesses the accuracy of such FT products relative to surface temperature. Utilizing retrievals from the soil moisture active/passive (SMAP), advanced microwave scanning radiometer, and special sensor microwave imager instruments, surface FT records have previously been derived globally. Moderate Resolution Imaging Spectroradiometer skin temperature, North American Land Data Assimilation System (NLDAS) skin, 0-10-cm soil layer, and 2-m air temperatures are compared to the various FT state products (FTSPs) by defining the threshold for FT state transitions at 0 °C. This paper utilizes the 2015-2016 overlap period in FT records within the NLDAS domain. Spatial variability of classification accuracy (CA) is then investigated over the study area. A proportional differencing method also enables the identification of biases between FTSPs and surface temperature variables. Additionally, by analyzing probability distribution functions of FTSPs associated with temperature values, we assess the distribution of temperature variables as they relate to FT classifications. Classification agreement is shown to vary with sensor configuration, seasonality, and retrieval time. Air temperature is found to have the highest CAs across FTSPs (81%-91%), while NLDAS soil exhibits a close relationship to FTSPs, especially in regard to SMAP products. Finally, ascending (p.m.) retrievals are shown to be increasingly linked to the selected temperature parameters as compared to descending (a.m.) observations. This paper contributes to an improved understanding of current FTSPs and will benefit efforts to enhance future FT products.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Automated Ice-Bottom Tracking of 2D and 3D Ice Radar Imagery Using Viterbi
           and TRW-S
    • Authors: Victor Berger;Mingze Xu;Mohanad Al-Ibadi;Shane Chu;David Crandall;John Paden;Geoffrey Charles Fox;
      Pages: 3272 - 3285
      Abstract: Multichannel radar depth sounding systems are able to produce two-dimensional (2D) and three-dimensional (3D) imagery of the internal structure of polar ice sheets. Information such as ice thickness and surface elevation is extracted from these data and applied to research in ice flow modeling and ice mass balance calculations. Due to a large amount of data collected, we seek to automate the ice-bottom layer tracking and allow for efficient manual corrections when errors occur in the automated method. We present improvements made to previous implementations of the Viterbi and sequential tree-reweighted message passing (TRW-S) algorithms for ice-bottom extraction in 2D and 3D radar imagery. These improvements are in the form of novel cost functions that allow for the integration of further domain-specific knowledge into the cost calculations and provide additional evidence of the characteristics of the ice sheets surveyed. Along with an explanation of our modifications, we demonstrate the results obtained by our modified implementations of the two algorithms and by previously proposed solutions to this problem, when compared to manually corrected ground truth data. Furthermore, we perform a self-assessment of tracking results by analyzing differences in the estimated ice-bottom for surveyed locations where flight paths have crossed and, thus, two separate measurements have been made at the same location. Using our modified cost functions and preprocessing routines, we obtain significantly decreased mean error measurements from both algorithms, such as a 47% reduction in average tracking error in the case of 3D imagery between the original and our proposed implementation of TRW-S.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Intercomparison of AMSR2- and MODIS-Derived Land Surface Temperature Under
           Clear-Sky Conditions
    • Authors: Cheng Huang;Si-Bo Duan;Xiao-Guang Jiang;Xiao-Jing Han;Hua Wu;Maofang Gao;Pei Leng;Zhao-Liang Li;
      Pages: 3286 - 3294
      Abstract: Land surface temperature (LST) is an important parameter in various fields, including hydrological, meteorological, and agricultural studies. Passive microwave techniques provide a practicable method to retrieve LST under both clear and cloudy conditions. In this study, LST derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature data during nighttime in the period 2015-2016 using a physically-based algorithm was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) LST product MYD11A1 over 16 study sites that represent four different land cover types, i.e., barren/sparsely vegetated, grasslands, croplands, and evergreen broadleaf forest. Compared to MODIS-derived LST, the root-mean-square error (RMSE) of AMSR2-derived LST is 6.0 K and the bias is 4.4 K over all study sites. For barren/sparsely vegetated sites, LST was overestimated by 6.7 K. To eliminate the systematic bias induced by the penetration depth effect of microwave radiation over barren/sparsely vegetated sites, a linear regression between AMSR2- and MODIS-derived LST was applied and the RMSE decreases from approximately 7.8 to 3.5 K. For the other three land cover types, the bias ranges from approximately 1.4 to 4.2 K and the RMSE ranges from approximately 2.1 to 5.9 K. The bias between AMSR2- and MODIS-derived LST is related to vegetation coverage. The value of bias increases with the decrease of normalized difference vegetation index. Furthermore, the RMSE has a strong dependency on precipitable water vapor (PWV). It presents a descending pattern of RMSE with the increase of PWV.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Crop Type Identification and Mapping Using Machine Learning Algorithms and
           Sentinel-2 Time Series Data
    • Authors: Siwen Feng;Jianjun Zhao;Tingting Liu;Hongyan Zhang;Zhengxiang Zhang;Xiaoyi Guo;
      Pages: 3295 - 3306
      Abstract: In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98%, respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Reconstructing All-Weather Land Surface Temperature Using the Bayesian
           Maximum Entropy Method Over the Tibetan Plateau and Heihe River Basin
    • Authors: Shuo Xu;Jie Cheng;Quan Zhang;
      Pages: 3307 - 3316
      Abstract: Land surface temperature (LST) is an important parameter for measuring the water-heat balance on the Earth's surface. Remote sensing technology provides a unique way to monitor LSTs over large spatial areas. Thermal infrared (TIR)-derived LST has a high spatial resolution and accuracy, but missing values caused by clouds hinder applications of this method. Passive microwave radiation can penetrate clouds, but the data have a relatively lower spatial resolution and accuracy. The Bayesian maximum entropy (BME) method was used for blending the Moderate Resolution Imaging Spectroradiometer (MODIS) LST and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) LST to produce a spatially complete, accurate, and high-spatial-resolution LST over the Tibetan Plateau (TP) and the Heihe River Basin (HRB). The accuracy of the BME method was validated using the adjusted soil temperature collected from the two verification regions. The root-mean-square errors (RMSEs) are less than 3.54 and 4.89 K over the relatively flat verification region during nighttime and daytime, respectively. In another rugged terrain verification region, the RMSE is less than 3 K during nighttime and ranges from 4.2 to 8.29 K during daytime. The RMSE of the blended LST is significantly better than that of a recent study of the AMSR-E LST retrieved during nighttime, and the RMSE is comparable to the AMSR-E LST retrieved during daytime. The BME method was adopted to integrate the MODIS LST and AMSR-E LST acquired on November 30, 2010 over the TP and HRB. The spatial completeness of the blended LST reached 100%, and the blended LST spatial pattern was generally consistent with the spatial patterns of the MODIS LST and AMSR-E LST. This paper demonstrates the utility of generating all-weather regional LSTs using the BME method from TIR LST and microwave LST products.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Spatio-Temporal Analysis of Urban Heat Island Using Multisource Remote
           Sensing Data: A Case Study in Hangzhou, China
    • Authors: Yuzhou Zhang;Jie Cheng;
      Pages: 3317 - 3326
      Abstract: In this paper, the spatio-temporal variations of the urban heat island (UHI) in Hangzhou are analyzed using multisource remote sensing data. The annual human settlement index (HSI) was derived from the annual MODIS NDVI and DMSP-OLS datasets from 2000 through 2013. The spatio-temporal patterns of HSI as well as the longitudinal variations of land surface temperature (LST) versus HSI were analyzed. The surface urban heat island intensity (SUHII) was derived by fitting a linear relationship between LST and HSI and it denotes the UHI effect. The spatial variation of SUHII is consistent with the distribution of HSI. According to the district division of Hangzhou, the SUHII of northeast Hangzhou is higher than that of southwest Hangzhou, and the UHI enhancement in the most developed districts of Hangzhou city, Yuhang and Xiaoshan, is remarkable. These results suggest that Hangzhou has a strong UHI effect and that the UHI has local regional characteristics. The UHI effect enhancement is mainly due to the expansion of the urban area, urban anthropogenic activities, and heat emission of the built-up area. According to the changes in SUHII for the period from 2000 to 2013, the UHI effect is intensifying in Hangzhou and measures for reducing the SUHII needs to be taken. To our knowledge, this is the first time that the spatio-temporal variation of the UHI in Hangzhou has been quantitatively analyzed using long time series of multisource remote sensing data, which can help understand the evolution of UHI and guide urban planning and development.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Validation of Satellite-Derived Sensible Heat Flux for TERRA/MODIS Images
           Over Three Different Landscapes Using Large Aperture Scintillometer and
           Eddy Covariance Measurements
    • Authors: Md Masudur Rahman;Wanchang Zhang;
      Pages: 3327 - 3337
      Abstract: Sensible heat flux (H) is the key component of the earth surface energy balance, which plays a vital role in the evolution of regional climate. The regional and global scale estimation of H is critical and challenging due to the validation of satellite-based estimation of surface energy balance components. This paper focused on the validation and performance evaluation of the satellite-derived H based on a single source remote sensing model in regional scale over three distinct landscapes in northwest China during May to September 2015 using data from TERRA-MODIS and meteorological observations. The results indicated that the remote sensing model with MODIS scenes performed well for the purpose of H estimation, but the influence of different landscapes was noticeable. The satellite-derived H (MODIS_H) was compared with the large aperture scintillometer (LAS)-measured H (LAS_H) and eddy covariance (EC)-measured H (EC_H) over the three different land surfaces. The root-mean-square errors (RMSE) of MODIS_H with respect to LAS_H were 31.63 W/m2 over alpine grassland, 44.07 W/m2 over croplands, and 112.98 W/m2 over mixed forests. The aggregated values (from May to September) of mean and standard deviation showed that the MODIS_H was moderately overestimated with fewer fluctuation over alpine grasslands, slightly overestimated with moderate fluctuation over croplands, and highly overestimated with higher fluctuation over mixed forests. The larger RMSE and over estimations could be explained by the vegetation heterogeneity, the wind speed profiles, and the complicated thermodynamic states.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Impact of Urban Cover Fraction on SMOS and SMAP Surface Soil Moisture
           Retrieval Accuracy
    • Authors: Nan Ye;Jeffrey P. Walker;Christoph Rüdiger;Dongryeol Ryu;Robert J. Gurney;
      Pages: 3338 - 3350
      Abstract: Both the European Space Agency's soil moisture and ocean salinity (SMOS) mission and the National Aeronautics and Space Administration's soil moisture active passive (SMAP) mission employ L-band (1.413 GHz) radiometers to observe brightness temperatures at ~40-km spatial resolution to subsequently derive global soil moisture every two to three days with a target accuracy of 0.04 m3/m3. However, the man-made structures that dominate urban areas in many of the SMOS and SMAP radiometers pixels may confound the interpretation of their radiometric observations if not taken into account, and thus, degrade the soil moisture retrieval accuracy. This paper investigates the effect that urban areas are expected to have on the SMOS and SMAP soil moisture retrieval accuracy using experimental data from the Australian airborne field campaigns performed over the past six years. Taking the total radiometric error budgets for the SMOS (3.95 K) and the SMAP (1.3 K) missions as conservative benchmarks for radiometric “error” that can be tolerated to achieve the 0.04 m3/m3 target accuracy, urban fraction thresholds of 6.6% and 2.2% were obtained for the SMOS and SMAP pixels, respectively, under warm dry (soil moisture
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Validation of a New Root-Zone Soil Moisture Product: Soil MERGE
    • Authors: Kenneth J. Tobin;Wade T. Crow;Jianzhi Dong;Marvin E. Bennett;
      Pages: 3351 - 3365
      Abstract: Soil MERGE (SMERGE) is a 0.125°, root-zone soil moisture (RZSM) product (0-40-cm depth) generated within the contiguous United States (CONUS). This product is developed by merging RZSM output from the North American land data assimilation system (NLDAS) with surface satellite retrievals from the European Space Agency Climate Change Initiative. SMERGE, at present, spans four decades (1979-2016). Here, we introduce the SMERGE approach and describe the validation of SMERGE RZSM estimates using three geophysical observations: 1) comparison with sparse in situ soil moisture data acquired from the soil climate analysis network (SCAN) and the U.S. Climate Reference Network (USCRN); 2) ranked correlation analysis against normalized difference vegetation index (NDVI) datasets; and 3) ranked correlation analysis of antecedent RZSM with storm-event streamflow across a range of precipitation intensities (5-45 mm/day). Relative to in situ SCAN and USCRN observations, SMERGE has an average daily correlation of 0.7-0.8 and unbiased root-mean square error close to 0.04 m3/m3-a level that is commonly applied as a validation target for large-scale soil moisture datasets. NDVI benchmarking allows us to indirectly evaluate SMERGE across CONUS and reveals it can predict near-term vegetation health anomalies with skill comparable to that of RZSM products generated by more complex data assimilation methods. In addition, streamflow-based evaluation results demonstrate that SMERGE antecedent RZSM can be used as a reliable predicator of storm-event runoff efficiency for rainfall events>25 mm/day.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • A Comparison of Two Soil Moisture Products S2MP and
           Copernicus-SSM Over Southern France
    • Authors: Hassan Bazzi;Nicolas Baghdadi;Mohammad El Hajj;Mehrez Zribi;Hatem Belhouchette;
      Pages: 3366 - 3375
      Abstract: This paper presents a comparison between the Sentinel-1/Sentinel-2-derived soil moisture product at plot scale (S2MP) and the new Copernicus surface soil moisture (C-SSM) product at 1-km scale over a wide region in southern France. In this study, both products were first evaluated using in situ measurements obtained by the calibrated time delay reflectometer in field campaigns. The accuracy against the in situ measurements was defined by the correlation coefficient R, the root mean square difference (RMSD), and the bias and the unbiased root mean square difference (ubRMSD). Then, the soil moisture estimations from both SSM products were intercompared over one year (October 2016-October 2017). Both products show generally good agreement with in situ measurements. The results show that using in situ measurements collected over agricultural areas and grasslands, the accuracy of the C-SSM is good (RMSD = 6.0 vol%, ubRMSD = 6.0 vol%, and R = 0.48) but less accurate than the S2MP (RMSD = 4.0 vol%, ubRMSD = 3.9 vol%, and R = 0.77). The intercomparison between the two SSM products over one year shows that both products are highly correlated over agricultural areas that are mainly used for cereals (R value between 0.5 and 0.9 and RMSE between 4 and 6 vol%). Over areas containing forests and vineyards, the C-SSM values tend to overestimate the S2MP values (bias> 5 vol%). In the case of well-developed vegetation cover, the S2MP does not provide SSM estimations while C-SSM sometimes provides underestimated SSM values.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Drought Monitoring and Evaluation by ESA CCI Soil Moisture Products Over
           the Yellow River Basin
    • Authors: Linqi Zhang;Yi Liu;Liliang Ren;Shanhu Jiang;Xiaoli Yang;Fei Yuan;Menghao Wang;Linyong Wei;
      Pages: 3376 - 3386
      Abstract: The multisatellite-retrieved soil moisture (SM) products released by the Europe Space Agency's Climate Change Initiative (ESA CCI) program have been widely used in numerous fields, including drought monitoring. In this article, a cumulative distribution function is applied to match the simulated SM from the Variable Infiltration Capacity (VIC) model and fill in the missing records of ESA CCI SM. The weekly standard SM index (SSI) calculated from the ESA CCI SM dataset is utilized to monitor the agricultural drought over the Yellow River Basin (YRB) during 2000-2012. The performance of the ESA CCI SSI is compared with the Standard Precipitation Index (SPI), the Standard Precipitation Evapotranspiration Index (SPEI), the self-calibrating Palmer Drought Severity Index based on VIC model (VIC-scPDSI) and the anomalies of ESA CCI SM and Normalized Difference Vegetation Index (NDVI). The results show that the interpolated ESA CCI SM is significantly (p
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Evaluating the Operational Application of SMAP for Global Agricultural
           Drought Monitoring
    • Authors: Iliana E. Mladenova;John D. Bolten;Wade T. Crow;Nazmus Sazib;Michael H. Cosh;Compton J. Tucker;Curt Reynolds;
      Pages: 3387 - 3397
      Abstract: Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Estimation and Mitigation of Time-Variant RFI Based on Iterative Dual
           Sparse Recovery in Ultra-Wide Band Through-Wall Radar
    • Authors: Yongping Song;Jun Hu;Tian Jin;Zhi Li;Ning Chu;Zhimin Zhou;
      Pages: 3398 - 3411
      Abstract: This paper focuses on the time-variant radio frequency interference (RFI) issue that ultra-wide band (UWB) through-wall radar (TWR) is faced with, and presents an iterative dual sparse recovery (IDSR) framework to combat it. The framework consists of two stages: 1) RFI estimation and detection and 2) IDSR of scattered echoes from objects and RFI signals. In the first stage, an overlapped short time Fourier transform is employed to construct and update the discrete frequency Doppler spectrum (DFDS). Then, a minimum statistic operation is conducted on the DFDS to estimate RFI signals, followed by detection via a 1-D cell-averaging constant false alarm rate detector to determinate whether RFI signals exist or not. In the second stage, a dual sparse model of the collected signals is set up, based on the fast-time frequency sparsity of RFI signals because of their narrow bands and the Doppler frequency sparsity of scattered echoes from objects because of their limited moving velocities. The alternating direction method of multipliers (ADMM) is introduced to iteratively and alternately recover RFI signals and scattered echoes from objects. Specifically, the iterative hard thresholding (IHT) method is used to complete the two sparse recovery operations. The involved two sparse dictionaries are simple and only made up of inverse discrete Fourier transform basis and independent of the received signals. The IDSR framework is able to relieve the influences of RFI signals and scattered echoes from objects on each other, and thus can reconstruct the two type signals to the maximum limit. Field experiments using a UWB TWR were carried out to verify the proposed method.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Coherent Detection Algorithm for Radar Maneuvering Targets Based on
           Discrete Polynomial-Phase Transform
    • Authors: Cunsuo Pang;Shengheng Liu;Yan Han;
      Pages: 3412 - 3422
      Abstract: Radar detection of maneuvering targets with high velocity and acceleration often results in severely deteriorative performance or even failure. Parameter-search approaches are commonly used to overcome such difficulty, but the substantial computational load involved makes them unable to meet the real-time requirements of practical systems. In this paper, to improve the detection performance for maneuvering targets at no expense of computing resource, we derive a coherent detection algorithm for maneuvering targets based on the discrete polynomial-phase transform. The proposed algorithm simultaneously decouples the displacement and velocity of target and reduces the order of the acceleration-induced quadratic phase in the frequency domain of the pulse compressed echoes. As such, the range and Doppler walks are both compensated without any a priori knowledge. In addition, the impact of the cross terms on the detection of auto-terms is investigated in the multiple targets scenario, and the applicable conditions of the proposed algorithm are given. Using numerical simulation and real-data experiments, we demonstrate the advantages of the proposed algorithm over state-of-the-art methods in terms of integration gain and estimation accuracy.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • A Saliency Detector for Polarimetric SAR Ship Detection Using Similarity
           Test
    • Authors: Xing-Chao Cui;Yi Su;Si-Wei Chen;
      Pages: 3423 - 3433
      Abstract: Ship detection in polarimetric SAR (PolSAR) image plays an important role in marine monitoring. From the viewpoint of the attention mechanism, ship targets can be salient candidates from sea clutter. A novel saliency detector for PolSAR ship detection has been proposed in this paper. The core idea is to explore the different scattering mechanisms between ships and sea clutter in low and medium sea conditions. The scattering mechanism differences are measured by the similarity test of polarimetric covariance matrices. A new saliency feature named similar pixel number (SPN) is proposed by recording the similar pixels within a moving window and a saliency detection method is developed thereafter. The proposed ship detection method mainly contains three steps. Firstly, the similarity test is applied between the central pixel and its neighborhood. Then, SPN feature is generated based on the result of similarity test by counting the number of similar pixels. Finally, with thresholding and morphological filtering procedures, ship targets can be extracted from the saliency feature map. Experimental studies with Radarsat-2 and GaoFen-3 data validate the advantages of the proposed method. Quantitative comparisons illustrate that the proposed method achieves the best detection performance with detection quality factors higher than 97% for both datasets over inshore dense and offshore sparse areas.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Effect Analysis and Spectral Weighting Optimization of Sidelobe Reduction
           on SAR Image Understanding
    • Authors: Zhihuo Xu;Heng-Chao Li;Quan Shi;Han Wang;Ming Wei;Jiajia Shi;Yeqin Shao;
      Pages: 3434 - 3444
      Abstract: This paper focuses on effect analysis and spectral weighting optimization of sidelobe reduction on synthetic aperture radar (SAR) image understanding. First, a statistical model for each pixel in complex-amplitude distribution of the SAR image is derived to investigate how the sidelobes of the nearby scatterers change the speckle noise. Second, the phase error is analyzed for interferometry applications when high sidelobe is in the presence. The potential benefit of using a sidelobe reduction method for improving interferometric phase accuracy is also investigated. Third, to better understand how the sidelobes affect the quality of SAR images at different sidelobe levels, with the same imaging resolution, one novel type of weights is optimized to improve the performance of spectral weighting for SAR sidelobe reduction by using one bioinspired method. Intensive sidelobe reduction experiments are carried out, and the results are verified on the probability density function (PDF), the level of speckle noise, and interferometric phase accuracy of the SAR images. The experimental results indicate that the sidelobe reduction methods can change the speckle noise and the statistical distributions for the underlying SAR imagery. One of the interesting findings is that a better equivalent number of looks (ENL) and interferometric phase accuracy can be obtained by using the optimized spectral weighting. However, the spatially variant apodization damages the PDFs of the SAR data, decreases the ENL of the SAR images, and leads to significant phase distortion for SAR interferometry.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Registrating Oblique SAR Images Based on Complementary Integrated
           Filtering and Multilevel Matching
    • Authors: Guobiao Yao;Xiaocheng Man;Li Zhang;Kazhong Deng;Huifu Zhuang;Guoqiang Zheng;
      Pages: 3445 - 3457
      Abstract: This paper presents a novel registration method for oblique synthetic aperture radar (SAR) images based on complementary integrated filtering (CIF) and multilevel matching. Our algorithm is divided into three steps. First, we considered different type of noises and employed the CIF to increase the signal-to-noise ratio of SAR images. Second, complementary affine invariant features, namely maximally stable extremal regions and Harris-affine features, were extracted simultaneously from image pairs, and then the initial matches were obtained based on the scale invariant feature transform (SIFT) descriptor and Euclidean distance. Therefore, the fundamental and homography matrixes could be calculated between image pairs, and then more matches were obtained under quasi-affine invariant SIFT (QAISIFT) and the hybrid geometric constraints. We further implemented the least square matching (LSM) based on the second-polynomial geometric model (SPGM), and thus the matching error of each corresponding point can be compensated according to the optimal SPGM. Third, the precise registration was achieved based on the matches of the second step. Experiments on four groups of oblique SAR images demonstrated the effectiveness of the proposed method. The contribution of this paper includes three aspects. One is that the proposed CIF can remove SAR image noise as much as possible; another is that the proposed QAISIFT can achieve near affine invariance across viewpoint change images; the third is that the advanced LSM can notably improve the accuracy of feature matches.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • An Analysis of Spatiotemporal Baseline and Effective Spatial Coverage for
           Lunar-Based SAR Repeat-Track Interferometry
    • Authors: Jinglong Dong;Qiang Shen;Liming Jiang;Houjun Jiang;Dewei Li;Hansheng Wang;Song Mao;
      Pages: 3458 - 3469
      Abstract: Lunar-based synthetic aperture radar repeat-track interferometry (LB-SAR RTI) is expected to play a significant role in addressing science issues pertaining to Earth dynamics because of its large-scale, long-term, and stable Earth observation (EO) ability. The distribution of the spatiotemporal baseline and effective spatial coverage are of great importance in the observation mode design and data acquisitions plan of LB-SAR RTI. Here, we describe the observation geometry and present the calculation method of the spatiotemporal baseline and effective spatial coverage of LB-SAR RTI. Using the Jet Propulsion Laboratory DE430 ephemeris, the results show that, first, the 1, 26, and 27 repeat cycles temporal baselines have the highest number of effective interferometric combinations, accounting for 50% of the effective observations; second, the optimal temporal baselines of LB-SAR RTI for large-scale EO are 26 and 27 repeat cycles, and the effective spatial coverage could reach up to 360° × 40° (longitude × latitude) in one day; and third, LB-SAR RTI can be used to observe the Earth's surface between latitudes 80° N and 80° S with different frequencies of observation that reach a maximum at latitudes 40° N and 40° S and decrease gradually toward the polar and equator regions. The lunar-based observation plan should take into account the initial observation time, temporal baseline, and effective spatial coverage.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Sentinel-2 Level-1 Radiometry Assessment Using Vicarious Methods From
           DIMITRI Toolbox and Field Measurements From RadCalNet Database
    • Authors: Bahjat Alhammoud;Jan Jackson;Sebastien Clerc;Manuel Arias;Catherine Bouzinac;Ferran Gascon;Enrico G. Cadau;Rosario Q. Iannone;Valentina Boccia;
      Pages: 3470 - 3479
      Abstract: The Sentinel-2A and Sentinel-2B constellation is an Earth observation optical mission developed and operated by the European Space Agency in the frame of the Copernicus program of the European Commission. The novel observation capacity offered by the multispectral instruments (MSI) on-board Sentinel-2 mission provides a massive resource for the Earth observation community. The accurate radiometric calibration is a critical element of the success of the mission; therefore, in-orbit calibration and validation activities are conducted within the Sentinel-2 Mission Performance Centre. The database of the imaging multispectral instrument and tool for radiometric intercomparison and the ground measurements of the radiometric calibration network (RadCalNet) are used to perform the vicarious validation of the radiometry of the Level-1 products delivered to the users. The aims of this validation are to assess the quality of the data product, to monitor the evolution of the radiometry of both instruments, and to ensure that the products meet the mission requirement accuracy. This article outlines the vicarious methods-Rayleigh scattering, desert pseudoinvariant calibration sites, sensor-to-sensor intercalibration, and the ground reflectance-based approach-that are used for the Sentinel-2 radiometry validation. The validation results indicate good performance and stability of both sensors MSI-A and MSI-B and consistency up to ~1% (~2% for red-edge bands) between them. The results of the intercomparison with the in situ measurements from RadCalNet illustrate the relevance of such dataset for the vicarious validation activity.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Cascaded Detection Framework Based on a Novel Backbone Network and Feature
           Fusion
    • Authors: Zhuangzhuang Tian;Wei Wang;Ronghui Zhan;Zhiqiang He;Jun Zhang;Zhaowen Zhuang;
      Pages: 3480 - 3491
      Abstract: Due to the ability of powerful feature representation, deep-learning-based object detection has attracted considerable research attention, and many methods have been proposed for remote sensing images. However, there are still some problems that need to be addressed. In this paper, a novel and effective detection framework based on faster region-based convolutional neural network is designed. Specifically, first, in order to locate the boundaries of large objects and find the missing small objects, DetNet is incorporated into the detection framework as the backbone network. DetNet fixes the spatial resolution in deep layers and adopts dilated bottleneck with convolution projection to increase the divergence between input and output feature maps. Then, the proposed framework uses the backbone network to extract the scene features and region features simultaneously, which are both mapped to feature vectors and then fused together. The feature fusion operation can improve the feature representation of the generated region. Last, to improve the performance of localization, the cascade structure is adopted in the framework. The cascade structure has multiple phases and every phase has independent classifier and regressor. The results obtained from the previous phase are used as the regions of interest in the next phase. Therefore, the multiphase detector can increase the detection accuracy phase by phase. Comprehensive evaluations on a public ten-class object detection dataset demonstrate the effectiveness of the proposed framework. Moreover, ablation experiments are also implemented to show the respective influence of different parts of the framework on the performance improvement.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • High-Resolution Aerial Images Semantic Segmentation Using Deep Fully
           Convolutional Network With Channel Attention Mechanism
    • Authors: Haifeng Luo;Chongcheng Chen;Lina Fang;Xi Zhu;Lijing Lu;
      Pages: 3492 - 3507
      Abstract: Semantic segmentation is one of the fundamental tasks in understanding high-resolution aerial images. Recently, convolutional neural network (CNN) and fully convolutional network (FCN) have achieved excellent performance in general images' semantic segmentation tasks and have been introduced to the field of aerial images. In this paper, we propose a novel deep FCN with channel attention mechanism (CAM-DFCN) for high-resolution aerial images' semantic segmentation. The CAM-DFCN architecture follows the mode of encoder-decoder. In the encoder, two identical deep residual networks are both divided into multiple levels and acted on spectral images and auxiliary data, respectively. Then, the feature map concatenation is carried out at each level. In the decoder, the channel attention mechanism (CAM) is introduced to automatically weigh the channels of feature maps to perform feature selection. On the one hand, the CAM follows the concatenated feature maps at each level to select more discriminative features for classification. On the other hand, the CAM is used to further weigh the semantic information and spatial location information in the adjacent-level concatenated feature maps for more accurate predictions. We evaluate the proposed CAM-DFCN by using two benchmarks (the Potsdam set and the Vaihingen set) provided by the International Society for Photogrammetry and Remote Sensing. Experimental results show that the proposed method has considerable improvement.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Aggregated Deep Fisher Feature for VHR Remote Sensing Scene Classification
    • Authors: Boyang Li;Weihua Su;Hang Wu;Ruihao Li;Wenchang Zhang;Wei Qin;Shiyue Zhang;
      Pages: 3508 - 3523
      Abstract: With the development of very high resolution satellite image acquisition technology, remote sensing scene classification has become an important and challenging task. In this article, aiming at tackling this task, we propose a hybrid architecture, i.e., aggregated deep Fisher feature (ADFF), which can make full use of deep convolutional features' rich semantic information and unsupervised encoding's high robustness. Unlike the previous methods, we first explore the optimal encoding layer in the pretraining CNN model, which naturally fuses the local and global image information in a novel way, making the ability of semantic acquisition further enhanced. ADFF can learn more suitable internal features from the remote sensing data, boosting the final performance. We evaluate our algorithm based on several public datasets, and the results show that our approach achieves superior performance compared with the state-of-the-art methods. The proposed ADFF obtains average classification accuracy of 98.81%, 95.21%, 86.01%, and 88.79%, respectively, on the UC Merced Land-Use, RSSCN7, NWPU-RESISC45 (10% for training), and NWPU-RESISC45 (20% for training) datasets.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Incremental Learning for Semantic Segmentation of Large-Scale Remote
           Sensing Data
    • Authors: Onur Tasar;Yuliya Tarabalka;Pierre Alliez;
      Pages: 3524 - 3537
      Abstract: In spite of remarkable success of the convolutional neural networks on semantic segmentation, they suffer from catastrophic forgetting: a significant performance drop for the already learned classes when new classes are added on the data having no annotations for the old classes. We propose an incremental learning methodology, enabling to learn segmenting new classes without hindering dense labeling abilities for the previous classes, although the entire previous data are not accessible. The key points of the proposed approach are adapting the network to learn new as well as old classes on the new training data, and allowing it to remember the previously learned information for the old classes. For adaptation, we keep a frozen copy of the previously trained network, which is used as a memory for the updated network in the absence of annotations for the former classes. The updated network minimizes a loss function, which balances the discrepancy between outputs for the previous classes from the memory and updated networks, and the misclassification rate between outputs for the new classes from the updated network and the new ground-truth. For remembering, we either regularly feed samples from the stored, little fraction of the previous data or use the memory network, depending on whether the new data are collected from completely different geographic areas or from the same city. Our experimental results prove that it is possible to add new classes to the network, while maintaining its performance for the previous classes, despite the whole previous training data are not available.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Object Tracking on Satellite Videos: A Correlation Filter-Based Tracking
           Method With Trajectory Correction by Kalman Filter
    • Authors: Yujia Guo;Daiqin Yang;Zhenzhong Chen;
      Pages: 3538 - 3551
      Abstract: Object tracking toward satellite videos faces various challenges, such as small size of the moving object, few texture, background similarities, etc. In this article, we propose a high-speed correlation filter (CF)-based tracker for object tracking on satellite videos. It takes advantage of the global motion characteristics of the moving target in satellite videos to constrain the tracking process, which is achieved by applying a Kalman filter (KF) to correct the tracking trajectory of the moving target. Thus, our tracker is named CFKF. Besides, a tracking confidence module is designed to pass information from the CF-based position detector to the KF-based trajectory corrector, and a constant optimized model update frequency is studied to speed up the tracker, as well as improve its performance. Furthermore, the target's orientation during the tracking process can be obtained by utilizing an orientation detector based on slope calculation. Experiments conducted on the satellite video datasets demonstrate that our tracker CFKF outperforms the other representative CF-based tracking methods in terms of accuracy and robustness and is also fast in speed.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • EOVNet: Earth-Observation Image-Based Vehicle Detection Network
    • Authors: Zhi Gao;Hong Ji;Tiancan Mei;Bharath Ramesh;Xiaodong Liu;
      Pages: 3552 - 3561
      Abstract: Vehicle detection from earth-observation (EO) image has been attracting remarkable attention for its critical value in a variety of applications. Encouraged by the stunning success of deep learning techniques based on convolutional neural networks (CNNs), which have revolutionized the visual data processing community and obtained the state-of-the-art performance in a variety of classification and recognition tasks on benchmark datasets, we propose a network, called EOVNet (EO image-based vehicle detection network), to bridge the gap between the advanced deep learning research of object detection and the specific task of vehicle detection in EO images. Our network has integrated nearly all advanced techniques including very deep residual networks for feature extraction, feature pyramid to fuse multiscale features, network for proposal generation with feature sharing, and hard example mining. Moreover, our novel designs of probability-based localization and homography-based data augmentation have been investigated, resulting in further improvement of the detection performance. For performance evaluation, we have collected nearly all the representative EO datasets associated with vehicle detection. Extensive experiments on the representative datasets demonstrate that our method outperforms the state-of-the-art object detection approach Faster R-CNN++ (which is based on the Faster R-CNN framework, but with significant improvement) with 5% average precision improvement. The source code will be made available after the review process.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Mining Concise Datasets for Testing Satellite-Data-Based Land-Cover
           Classifiers Meant for Large Geographic Areas
    • Authors: Tommy Chang;Avinash C. Kak;
      Pages: 3562 - 3577
      Abstract: Obtaining an accurate estimate of a land-cover classifier's performance over a wide geographic area is a challenging problem due to the need to generate the ground truth that represents the entire area, which may be thousands of square kilometers in size. The current best approach for solving this problem constructs a test set by drawing samples randomly from the entire area-with a human supplying the true label for each such sample-with the hope that the labeled data thus collected capture statistically all of the data diversity in the area. A major shortcoming of this approach is that, in an interactive session, it is difficult for a human to ensure that the information provided by the next data sample chosen by the random sampler is nonredundant with respect to the data already collected. In order to reduce the annotation burden caused by this uncertainty, it makes sense to remove any redundancies from the entire dataset before presenting its samples to the human for annotation. This article presents a framework that uses a combination of clustering and compression to create a concise-set representation of the land-cover data for a large geographic area. Whereas clustering is achieved by applying locality-sensitive hashing to the data elements, compression is achieved by choosing a single data element to represent a cluster. This framework reduces the annotation burden on the human and makes it more likely that the human would persevere during the annotation stage. We validate our framework experimentally by comparing it with the traditional random sampling approach using WorldView2 satellite imagery.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Unsupervised Change Detection in Multispectral Remote Sensing Images via
           Spectral-Spatial Band Expansion
    • Authors: Sicong Liu;Qian Du;Xiaohua Tong;Alim Samat;Lorenzo Bruzzone;
      Pages: 3578 - 3587
      Abstract: Due to the limited number of spectral channels in multispectral remote sensing images, change information, especially the multiclass changes, may be insufficiently represented, resulting in inaccurate detection of changes. In this paper, we propose to use unsupervised band expansion techniques to generate artificial spectral and spatial bands to enhance the change representation and discrimination for change detection (CD) from multispectral images. In particular, in the proposed approach, two simple nonlinear functions, i.e., multiplication and division, are applied for spectral expansion. Multiscale morphological reconstruction is used to extend the band spatial information. The expanded band sets are then used and validated in three popular unsupervised CD techniques for solving a multiclass CD problem. Experimental results obtained on three real bitemporal multispectral remote sensing datasets confirm the effectiveness of the proposed approach.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • A Reliable Mixed-Norm-Based Multiresolution Change Detector in
           Heterogeneous Remote Sensing Images
    • Authors: Redha Touati;Max Mignotte;Mohamed Dahmane;
      Pages: 3588 - 3601
      Abstract: Analysis of heterogeneous remote sensing image is a challenging and complex problem due to the fact that the local statistics of the data to be processed can be radically different. In this article, we present a novel and reliable unsupervised change detection (CD) method to analyze heterogeneous remotely sensed image pairs. The proposed method is based on an imaging modality-invariant operator that detects at different scale levels the differences in terms of high-frequency pattern of each structural region existing in the two heterogeneous satellite images. First, this new detector is based upon a dual-norm formulation that makes our underlying CD estimation particularly robust in terms of a sensitivity/specificity tradeoff. Second, the detection process, embedded in a multiresolution framework, allows us to estimate a robust similarity or difference map that is then filtered out by a superpixel-based spatially adaptive filter to further increase its reliability against noise. Finally, changes are then identified from this similarity map by a simple binary clustering process that also takes into account the spatial contextual information around each pixel. Experimental results involving different types of heterogeneous remotely sensed image pairs confirm the robustness of the proposed approach.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Texture Pattern Separation for Hyperspectral Image Classification
    • Authors: Bing Tu;Jinping Wang;Guoyun Zhang;Xiaofei Zhang;Wei He;
      Pages: 3602 - 3614
      Abstract: In this paper, a new spectral-spatial classification method based on texture pattern separation (TPS) is proposed for hyperspectral image (HSI) classification that consists of the following steps. First, the principal component analysis (PCA) method is used to reduce the dimensions of the original HSI. Then, the processed image is partitioned into several three-dimensional subcube images, each of which can be decomposed into a texture layer and background layer using joint convolutional analysis and synthesis sparse representation (JCAS) to reflect meaningful information and disturbing information, respectively. Next, texture layer images are fed into different kernel pixelwise classifiers for classification. Finally, majority voting is utilized to obtain the final classification result. Through comparisons with other well-known classification methods, the proposed TPS method shows outstanding performance, even with a quite limited number of training samples.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Semisupervised Hyperspectral Image Classification Using Deep Features
    • Authors: M. Said Aydemir;Gokhan Bilgin;
      Pages: 3615 - 3622
      Abstract: As in other remote-sensing applications, collecting ground-truth information from the earth's surface is expensive and time-consuming process for hyperspectral imaging. In this study, a deep learning-based semisupervised learning framework is proposed to solve this small labeled sample size problem. The main contribution of this study is the construction of a deep learning model for each hyperspectral sensor type that can be used for data obtained from these sensors. In the proposed framework, the “trained base model” is obtained with any dataset from a hyperspectral sensor, and fine-tuned and evaluated with another dataset. In this way, a general deep model is developed for extracting deep features which can be linearly classified or clustered. The system is evaluated with three different clustering techniques, the modified k-means, subtractive, and mean-shift clustering, for selecting initial representative labeled training samples comparatively. Another contribution of this study is to exploit the labeled and unlabeled sample information with linear transductive support vector machines. The proposed semisupervised learning framework is proven by the experimental results using different number of small sample sizes.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Label Propagation Ensemble for Hyperspectral Image Classification
    • Authors: Youqiang Zhang;Guo Cao;Ayesha Shafique;Peng Fu;
      Pages: 3623 - 3636
      Abstract: The imbalance between limited labeled pixels and high dimensionality of hyperspectral data can easily give rise to Hughes phenomenon. Semisupervised learning (SSL) methods provide promising solutions to address the aforementioned issue. Graph-based SSL algorithms, also called label propagation methods, have obtained increasing attention in hyperspectral image (HSI) classification. However, the graphs constructed by utilizing the geometrical structure similarity of samples are unreliable due to the high dimensionality and complexity of the HSIs, especially for the case of very limited labeled pixels. Our motivation is to construct label propagation ensemble (LPE) model, then use the decision fusion of multiple label propagations to obtain pseudolabeled pixels with high classification confidence. In LPE, random subspace method is introduced to partition the feature space into multiple subspaces, then several label propagation models are constructed on corresponding subspaces, finally the results of different label propagation models are fused at decision level, and only the unlabeled pixels whose label propagation results are the same will be assigned with pseudolabels. Meanwhile extreme learning machine classifiers are trained on the labeled and pseudolabeled samples during the iteration. Compared with traditional label propagation methods, our proposed method can deal with the situation of very limited labeled samples by providing pseudolabeled pixels with high classification confidence, consequently, the accurate base classifiers are obtained. To demonstrate the effectiveness of the proposed method, LPE is compared with several state-of-the-art methods on four hyperspectral datasets. In addition, the method that only use label propagation is investigated to show the importance of ensemble technique in LPE. The experimental results demonstrate that the proposed method can provide competitive solution for HSI classification.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Hyperspectral Anomaly Detection via Convolutional Neural Network and Low
           Rank With Density-Based Clustering
    • Authors: Shangzhen Song;Huixin Zhou;Yixin Yang;Jiangluqi Song;
      Pages: 3637 - 3649
      Abstract: Over the last two decades, anomaly detection (AD) has been known to play a critical role in hyperspectral image analysis, which provides a new way to distinguish the targets from the background without prior knowledge. Recently, the representation-based methods were proposed and soon became a significant type of methods on hyperspectral AD. In this paper, a novel AD algorithm based on convolutional neural network (CNN) and low-rank representation (LRR) is proposed. First, a CNN model is built and trained on hyperspectral image (HSI) datasets to accurately obtain the resulting abundance maps. Compared with the raw dataset, abundance maps contain more distinctive features to identify anomalies from the background. Second, a dictionary is constructed by the density-based spatial clustering of applications with noise (DBSCAN) algorithm to stably represent the background component. Third, a matrix decomposition method based on LRR is adopted. In this way, a coefficient matrix corresponding to the constructed dictionary is obtained, which is low rank. At the same time, a residual matrix can be obtained as well, which is sparse. Finally, anomalies can be extracted from the residual matrix. The experimental results show that the proposed method achieves a superior performance compared to some of the state-of-the-art methods in the field of hyperspectral AD.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • A Hybrid Statistics and Representation-Based Anomaly Detector for
           Hyperspectral Images
    • Authors: Lingxiao Zhu;Gongjian Wen;Shaohua Qiu;Xing Zhang;
      Pages: 3650 - 3664
      Abstract: Anomaly detection (AD) is an important research topic in the hyperspectral remote sensing field. However, owing to the complex background distributions and the interference of clutter noise in practical situations, the AD problem is far from being addressed satisfactorily. In this paper, a novel AD method by joining the statistical model and the representation theory for a predominant detection is proposed. It mainly consists of two parts. A Mahalanobis distance-based anomaly characterization criterion is first designed to acquire the initial detection result. In order to model the background accurately and efficiently, a fast matrix decomposition algorithm is utilized to eliminate the anomaly/noise information from the raw data. Moreover, both the decomposed components are taken into account in the measuring formulation for enhancing the distinguishability between anomalies and the background. Second, a local representation process is performed on some selected pixels according to an improved image segmentation method. By using an improved outlier determination criterion, some latent false alarms in these pixels can be located, and consequently their response values are suppressed effectively through an adaptive weight function. Experimental results on one synthetic and two real hyperspectral datasets validate the outstanding performance of our proposed method compared with some state-of-the-art anomaly detectors.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Hyperspectral Band Selection Using Weighted Kernel Regularization
    • Authors: Weiwei Sun;Gang Yang;Jiangtao Peng;Qian Du;
      Pages: 3665 - 3676
      Abstract: A band selection method named weighted kernel regularization (WKR) is proposed for hyperspectral imagery (HSI) classification. The WKR aims to select dissimilar and class-separable bands to better model the relationship between labeled samples. First, the WKR considers nonlinear structure of hyperspectral data and models nonlinear relations between HSI samples and their class labels using a weighted kernel ridge regression (WKRR) program with respect to sample coefficients. Second, it combines the L1 penalty term of weights on all bands with the above WKRR program into the unified framework of WKR. The L1 penalty term considers divergent contributions from different bands in describing nonlinear relations and guarantees the sparsity of band weights. Third, the WKR algorithm implements the KerNel Iterative-based Feature Extraction (KNIFE) algorithm to estimate the proper band weights. The KNIFE linearizes the nonlinear kernels to avoid high computational cost, and iteratively minimizes two convex subproblems with respect to the sample coefficients and band weights. Finally, the first k bands with larger weights and larger dissimilarity with other bands are automatically chosen to form the band subset. Experimental results show that the WKR outperforms the state-of-the-art methods in classification accuracies with a lower computational cost.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Infrared Small Target Detection Using Local and Nonlocal Spatial
           Information
    • Authors: Wei Li;Mingjing Zhao;Xiaoya Deng;Lu Li;Liwei Li;Wenjuan Zhang;
      Pages: 3677 - 3689
      Abstract: Existing infrared (IR) small target detection methods are divided into local priors-based and nonlocal priors-based ones. However, due to heterogeneous structures in IR images, using either local or nonlocal information is always suboptimal, which causes detection performance to be unstable and unrobust. To solve the issue, a comprehensive method, exploiting both local and nonlocal priors, is proposed. The proposed method framework includes dual-window local contrast method (DW-LCM) and multiscale-window IR patch-image (MW-IPI). In the first stage, DW-LCM designs dual-window to compensate for the shortcomings of local priors-based methods, which easily mistake some isolated weak-signal targets. In the second stage, MW-IPI utilizes several small windows with various sizes, which can not only decrease the redundant information generated by sliding windows, but also extract more discriminative information to prevent some pixels in the strong-border edge from being falsely detected. Then, multiplication pooling operation is employed to enhance the target separation and suppress the background clutter simultaneously. Experimental results using five real IR datasets with various scenes reveal the effectiveness and robustness of the proposed method.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Rapid Urban Roadside Tree Inventory Using a Mobile Laser Scanning System
    • Authors: Yiping Chen;Shiqian Wang;Jonathan Li;Lingfei Ma;Rongren Wu;Zhipeng Luo;Cheng Wang;
      Pages: 3690 - 3700
      Abstract: This paper presents a feasible workflow for use of three-dimensional point clouds acquired by a vehicle-borne mobile laser scanning (MLS) system for urban tree inventory. Extracting geometrical information, such as crown diameter, diameter at breast height (DBH), and tree height, from the MLS point clouds is a challenging task due to huge data volume, occlusions, mixed density, and irregular distribution of points in complex urban environments. The proposed workflow consists of three parts: individual tree cluster extraction, geometric parameter estimation, and tree species classification. The results show that over 93% of the roadside trees were correctly detected with an average error of about 5% in the DBH estimation when compared to field surveys and 78% of the overall accuracy was achieved for the classification of tree species.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Corrections to “Recognizing Global Reservoirs From Landsat 8 Images: A
           Deep Learning Approach” [Sep 19 3168-3177]
    • Authors: Weizhen Fang;Cunguang Wang;Xi Chen;Wei Wan;Huan Li;Siyu Zhu;Yu Fang;Baojian Liu;Yang Hong;
      Pages: 3701 - 3701
      Abstract: Presents corrections to above named paper.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • Corrections to “An Efficient and Effective Approach for Georeferencing
           AVHRR and GaoFen-1 Imageries Using Inland Water Bodies” [Jul 18
           2491-2500]
    • Authors: Siyu Zhu;Wei Wan;Hongjie Xie;Baojian Liu;Huan Li;Yang Hong;
      Pages: 3702 - 3702
      Abstract: Presents corrections to above named paper.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
  • SHARE AND MANAGE YOUR RESEARCH DATA
    • Pages: 3704 - 3704
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2019
      Issue No: Vol. 12, No. 9 (2019)
       
 
 
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