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  Subjects -> ELECTRONICS (Total: 175 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 76)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 305)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
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: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
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: 44)
China Communications     Full-text available via subscription   (Followers: 8)
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: 253)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 104)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 85)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 91)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 50)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 2)
EPJ Quantum Technology     Open Access  
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: 185)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 96)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
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: 65)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 69)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 55)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 19)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 39)
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: 70)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 11)
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 Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 45)
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: 57)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 24)
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: 12)
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: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 12)
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: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 12)
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: 24)
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: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
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: 23)
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: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 162)
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: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
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: 28)
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 Rekayasa Elektrika     Open Access  
Jurnal Teknik 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: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 18)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 6)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
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: 1)
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)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 53)
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: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
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: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
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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Journal Cover
Geoscience and Remote Sensing, IEEE Transactions on
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 185  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Geoscience and Remote Sensing publication information
    • Abstract: "Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication."
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: "Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication."
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • A Perspective on the Performance of the CFOSAT Rotating Fan-Beam
           Scatterometer
    • Authors: Wenming Lin;Xiaolong Dong;Marcos Portabella;Shuyan Lang;Yijun He;Risheng Yun;Zhixiong Wang;Xingou Xu;Di Zhu;Jianqiang Liu;
      Pages: 627 - 639
      Abstract: The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Study of Temperature Heterogeneities at Sub-Kilometric Scales and
           Influence on Surface–Atmosphere Energy Interactions
    • Authors: Vicente García-Santos;Joan Cuxart;Maria Antonia Jiménez;Daniel Martínez-Villagrasa;Gemma Simó;Rodrigo Picos;Vicente Caselles;
      Pages: 640 - 654
      Abstract: The retrieval of land surface temperature (LST) from remote sensing techniques has been studied and validated during the past 40 years, leading to important improvements. Accurate LST values are currently obtained through measurements using medium resolution thermal infrared (TIR) sensors. However, the most recent review reports demonstrated that the future TIR LST products need to obtain reliable temperature values at a high spatial resolution (100 m or higher) to study temperature variations between different elements in a heterogeneous kilometric area. The launch of high-resolution TIR sensors in the near future requires studies of the temporal evolution and spatial heterogeneities of the elements in a mixed region. The present study analyzes the LST in a sub-kilometric highly heterogeneous area, combining the use of LST products from high-resolution TIR orbiting sensors with the LST maps created from a TIR camera onboard an unmanned aerial vehicle (UAV). The aim is to estimate the LST variability in a heterogeneous area containing different surfaces (roads, buildings, and grass), observed from different TIR sensors at different spatial resolutions, covering from the meter to the kilometer scales. Several results showed that variations in the LST up to 18 °C were identified with the UAV-TIR camera, and significant differences were also present in the LST products obtained from simultaneous overpasses of high-resolution satellite TIR sensors. A second objective of the study, due to the availability of the high-resolution LST fields, was to explore the thermal advection between different elements and determine if it correlates with the surface energy budget in the same area, thus indicating that this process is of importance for heterogeneous terrains at these scales. This paper also highlights the relevance of the UAV-TIR camera flight for future studies since it is not commonly used in TIR remote sensing but has substantial potential advantage-.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Toward the Generation of a Wind Geophysical Model Function for Spaceborne
           GNSS-R
    • Authors: Wenming Lin;Marcos Portabella;Giuseppe Foti;Ad Stoffelen;Christine Gommenginger;Yijun He;
      Pages: 655 - 666
      Abstract: This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height (Hs) in account, particularly for low wind conditions. Consequently, a wind/Hs approach is proposed for improved wind retrievals.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • HSI-DeNet: Hyperspectral Image Restoration via Convolutional Neural
           Network
    • Authors: Yi Chang;Luxin Yan;Houzhang Fang;Sheng Zhong;Wenshan Liao;
      Pages: 667 - 682
      Abstract: The spectral and the spatial information in hyperspectral images (HSIs) are the two sides of the same coin. How to jointly model them is the key issue for HSIs’ noise removal, including random noise, structural stripe noise, and dead pixels/lines. In this paper, we introduce the deep convolutional neural network (CNN) to achieve this goal. The learned filters can well extract the spatial information within their local receptive filed. Meanwhile, the spectral correlation can be depicted by the multiple channels of the learned 2-D filters, namely, the number of filters in each layer. The consequent advantages of our CNN-based HSI denoising method (HSI-DeNet) over previous methods are threefold. First, the proposed HSI-DeNet can be regarded as a tensor-based method by directly learning the filters in each layer without damaging the spectral-spatial structures. Second, the HSI-DeNet can simultaneously accommodate various kinds of noise in HSIs. Moreover, our method is flexible for both single image and multiple images by slightly modifying the channels of the filters in the first and last layers. Last but not least, our method is extremely fast in the testing phase, which makes it more practical for real application. The proposed HSI-DeNet is extensively evaluated on several HSIs, and outperforms the state-of-the-art HSI-DeNets in terms of both speed and performance.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Frequency Controllable Envelope Operator and Its Application in Multiscale
           Full-Waveform Inversion
    • Authors: Zhaoqi Gao;Zhibin Pan;Jinghuai Gao;Ru-Shan Wu;
      Pages: 683 - 699
      Abstract: Full-waveform inversion (FWI) attempts to find optimal models of subsurface by using full information of the observed data. One difficulty in conventional FWI is that the misfit function has many local minima because of cycle skipping. Envelope inversion (EI), which uses the envelope operator (EO)-based misfit function, has been proven to be effective in mitigating cycle skipping and recovering long-wavelength velocity model. However, EI ignores the fact that the information within different frequency bands plays different roles in inversion. In this paper, a frequency controllable EO, which can control the frequency components being used to construct envelope, is proposed. We propose a new misfit function and a multiscale FWI method. Using synthetic experiments based on the Marmousi model, we demonstrate that the proposed method is better than EI in mitigating cycle skipping and in building an accurate initial model for conventional FWI to significantly improve its final result. In addition, this method can tolerate a wide range of noise levels. Its effectiveness has also been successfully demonstrated using a field data set.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Development of a New Algorithm to Identify Clear Sky MSU Data Using AMSU-A
           Data for Verification
    • Authors: Zeyi Niu;Xiaolei Zou;
      Pages: 700 - 708
      Abstract: Observations from the microwave sounding unit (MSU), from 1978 to 2006 and its successor, the Advanced Microwave Sounding Unit-A (AMSU-A, 1998-present), and onboard the National Oceanic and Atmospheric Administration's polar-orbiting satellites have been widely used for estimating global climate trends. The MSU has a long-term data set, but it is difficult to obtain cloud information like the cloud liquid water path (LWP) directly from this data set. To monitor and investigate the cloud effect on global upper air temperature trends using MSU observations, a cloud detection algorithm must be developed for the MSU. Considering the similar center frequencies of MSU channel 1 and AMSU-A channel 3, a new cloud detection algorithm is established based on the differences between observed and model-simulated brightness temperatures (O-B-μ(α)-μ(φ)) of MSU channel 1 or AMSU-A channel 3 over oceans, where μ(φ) is a latitudinal dependent global mean bias and μ(α) is the global mean bias depending on scan angle. If a data point satisfies the condition of O-B-μ(α)-μ(φ) ≥ 1 K, it is removed from the clear sky data set. In order to ensure that those points that are partially affected by the clouds, such as clear and cloud mixed fields-of-views located near cloud edges and/or within optically thin clouds, all data points within the 60-km radial distance of the detected point are removed. Validated with the AMSU-A derived LWP retrievals, about 50% of the clear sky data are successively identified and 99% of the cloudy radiances are successively removed for all NOAA-15 AMSU-A data on January 15, April 15, July 15, and October 15, 2002.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Tropical Cyclone Center Automatic Determination Model Based on HY-2 and
           QuikSCAT Wind Vector Products
    • Authors: Tangao Hu;Yiyue Wu;Gang Zheng;Dengrong Zhang;Yuzhou Zhang;Yao Li;
      Pages: 709 - 721
      Abstract: Tropical cyclones (TCs) are weather systems with vast destructive power. A key element in issuing warnings for TCs approaching land is the accurate and timely knowledge of the location of the circulation center. Current procedures are usually performed with manual input from a human analyst. Since subjective elements are involved in this process, analysts could disagree on the results even when multiple factors are considered. In this paper, we propose a new method for the automatic determination of the center of TCs using HY-2 and Quick Scatterometer wind vector products. First, we calculate the high-wind speed zone from the wind speed map. Next, we extract the vortexlike zone using the wind direction map. Finally, we automatically determine the center from the vortexlike zone. Six representative TCs (Haikui, Saola, Usagi, Florence, Ioke, and Gordon) and seventeen TCs in the 2013-2016 seasons are used to validate the TC center automatic determination (TCCAD) method. The results show that 1) the accuracy of the TCCAD method is close to that of the human expert method for most TCs and 2) the standard deviation in the TCCAD method is much smaller than that in the human expert method, which indicates that the TCCAD method is more efficient and reliable. Although the TCCAD method has some limitations because of the quality of scatterometer products and problems with the structure of TC eyes, it can automatically provide the practical, independent, and objective identification of TC centers.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Flood Mapping Based on Synthetic Aperture Radar: An Assessment of
           Established Approaches
    • Authors: Lisa Landuyt;Alexandra Van Wesemael;Guy J.-P. Schumann;Renaud Hostache;Niko E. C. Verhoest;Frieke M. B. Van Coillie;
      Pages: 722 - 739
      Abstract: In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral
           Image Classification
    • Authors: Mercedes E. Paoletti;Juan Mario Haut;Ruben Fernandez-Beltran;Javier Plaza;Antonio J. Plaza;Filiberto Pla;
      Pages: 740 - 754
      Abstract: Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral–spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral–spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral–spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image
           Classification
    • Authors: Nanjun He;Mercedes E. Paoletti;Juan Mario Haut;Leyuan Fang;Shutao Li;Antonio Plaza;Javier Plaza;
      Pages: 755 - 769
      Abstract: The classification of hyperspectral images (HSIs) using convolutional neural networks (CNNs) has recently drawn significant attention. However, it is important to address the potential overfitting problems that CNN-based methods suffer when dealing with HSIs. Unlike common natural images, HSIs are essentially three-order tensors which contain two spatial dimensions and one spectral dimension. As a result, exploiting both spatial and spectral information is very important for HSI classification. This paper proposes a new hand-crafted feature extraction method, based on multiscale covariance maps (MCMs), that is specifically aimed at improving the classification of HSIs using CNNs. The proposed method has the following distinctive advantages. First, with the use of covariance maps, the spatial and spectral information of the HSI can be jointly exploited. Each entry in the covariance map stands for the covariance between two different spectral bands within a local spatial window, which can absorb and integrate the two kinds of information (spatial and spectral) in a natural way. Second, by means of our multiscale strategy, each sample can be enhanced with spatial information from different scales, increasing the information conveyed by training samples significantly. To verify the effectiveness of our proposed method, we conduct comprehensive experiments on three widely used hyperspectral data sets, using a classical 2-D CNN (2DCNN) model. Our experimental results demonstrate that the proposed method can indeed increase the robustness of the CNN model. Moreover, the proposed MCMs+2DCNN method exhibits better classification performance than other CNN-based classification strategies and several standard techniques for spectral-spatial classification of HSIs.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Generation of Large-Scale Moderate-Resolution Forest Height Mosaic With
           Spaceborne Repeat-Pass SAR Interferometry and Lidar
    • Authors: Yang Lei;Paul Siqueira;Nathan Torbick;Mark Ducey;Diya Chowdhury;William Salas;
      Pages: 770 - 787
      Abstract: This paper provides an overview of the scattering model, inversion approach, and validation of the application results for creating large-scale moderate-resolution (hectare-level) mosaics of forest height through using spaceborne repeat-pass SAR interferometry and lidar. By incorporating several improvements to the forest height inversion and mosaicking approach, the height estimation accuracy along with the robustness of this approach have been considerably enhanced from its originally reported accuracy of RMSE of 3-4 m at a 20-hectare aggregated pixel size to RMSE of 3-4 m on the order of 3-6 hectares. Furthermore, practical data processing schemes are provided in detail. Extensive validation results are demonstrated which include: 1) a forest height mosaic (total area of 11.6 million hectares) is generated for the U.S. states of Maine and New Hampshire using Japanese Aerospace Exploration Agency's (JAXA) ALOS-1 InSAR correlation data and a small airborne lidar strip (44 000 hectares); 2) the mosaic height estimates are further compared with the available airborne lidar data and field measurements over both flat and mountainous areas; and 3) feasibility of using modern repeat-pass InSAR satellites with short repeat interval is also examined by using JAXA's ALOS-2 data. This simple and efficient approach is a potential observational prototype with much smaller error budget for the future spaceborne repeat-pass L-band InSAR systems with small spatial baseline and moderate/large temporal baseline (such as NISAR) in combination with lidar (such as GEDI) on the application of large-scale forest height/biomass mapping. It also serves as a complementary tool to the spaceborne single-pass InSAR systems using InSAR/PolInSAR methods when full-pol data are not available and/or when the underlying topography slope causes problems for these approaches.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Physics-Based Modeling of Active and Passive Microwave Covariations Over
           Vegetated Surfaces
    • Authors: Thomas Jagdhuber;Alexandra G. Konings;Kaighin A. McColl;Seyed Hamed Alemohammad;Narendra Narayan Das;Carsten Montzka;Moritz Link;Ruzbeh Akbar;Dara Entekhabi;
      Pages: 788 - 802
      Abstract: Active and passive low-frequency microwave measurements from a number of space- and airborne instruments are used to estimate soil moisture. Each of the sensing approaches has distinct advantages and disadvantages. There is increasing interest in combining active and passive measurements in order to realize the advantages and alleviate the disadvantages. In order to combine active and passive measurements, their covariations with respect to soil moisture need to be known. The covariation is dependent on how the active and passive microwaves interact with vegetation canopy and soil surface. In this paper, we introduce a physics-based model for the covariation of active and passive microwaves over soil surfaces with vegetation cover. The analytical form for a covariation function is derived which depends on the scattering and absorption of microwaves by soil and vegetation with different orientations, structures, and water contents. The main finding is that the covariation function β is related to the roughness and vegetation losses in the two measurements. An increase in soil roughness or in vegetation cover leads to less negative values of β, which is pronounced for dense and moist vegetation. Both the soil and vegetation components introduce a polarization dependence of β that is caused by polarization-induced differences in soil scattering and oriented plant structures. The forward modeled covariations are plotted together with statistically derived covariation estimates from two months of global active and passive L-band observations of the Soil Moisture Active Passive mission. The physically modeled and statistically derived estimates of covariation are comparable in magnitude and scale.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • An Adaptive Spectral Decorrelation Method for Lossless MODIS Image
           Compression
    • Authors: Feiyang Liu;Zhenzhong Chen;
      Pages: 803 - 814
      Abstract: Spectral decorrelation has been considered an important approach in multispectral image compression to remove the redundancy among bands. In this paper, we propose a novel adaptive spectral decorrelation method based on clustering analysis for Moderate Resolution Imaging Spectroradiometer (MODIS) image compression. The remote sensing image bands are divided into different clusters by the method based on density peak clustering. Then, reversible Karhunen–Loeve transform and polynomial least square estimation are employed to reduce the band redundancy, achieving an effective spectral decorrelation. As shown by the experimental results, our method achieves remarkable bit-saving when compared to the state-of-the-art algorithms on MODIS image data set.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Classification of Coral Reefs in the South China Sea by Combining Airborne
           LiDAR Bathymetry Bottom Waveforms and Bathymetric Features
    • Authors: Dianpeng Su;Fanlin Yang;Yue Ma;Kai Zhang;Jue Huang;Mingwei Wang;
      Pages: 815 - 828
      Abstract: Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Radiometry Calibration With High-Resolution Profiles of GPM: Application
           to ATMS 183-GHz Water Vapor Channels and Comparison Against Reanalysis
           Profiles
    • Authors: John Xun Yang;Hu Yang;
      Pages: 829 - 838
      Abstract: The reanalysis data produced by numerical weather prediction (NWP) models and data assimilation have been widely used for radiometer calibration. They provide atmospheric profiles that are necessary for radiative transfer simulation against observation. However, there are biases and uncertainties in the reanalysis due to NWP model mechanism, parameterization, boundary conditions, and assimilation skills. As spaceborne radiometer data have been used in deriving reanalyses, reanalyses are not independent of these radiometers and should be used with caution when used as reference for radiometer calibration. In addition, these data often have coarse spatial (~100 km horizontally) and temporal resolution (~6 h). An independent data set with high resolution can be very useful to diagnose reanalyses and might improve calibration. The Global Precipitation Measurement (GPM) core observatory measures atmospheric water signatures with an onboard radar and radiometer. A GPM data set including atmospheric water vapor, cloud liquid water, and precipitation has been produced based on observational retrieval with high spatiotemporal resolution (~5 km horizontally and 250 m vertically). We have developed a scheme to ingest the high-resolution GPM profiles and perform rigorous simulation and calibration taking into account the radiometer spectral response function, footprint size variation, and antenna pattern. GPM data exhibit different water vapor profiles and weighting functions from reanalyses. It produces overall consistent results of calibration as reanalyses and outperforms them in some aspects. The GPM profiles and our scheme are very useful and will be routinely applied to monitor Advanced Technology Microwave Sounder inflight status.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Modeling of Thin-Cloud TOA Reflectance Using Empirical Relationships and
           Two Landsat-8 Visible Band Data
    • Authors: Haitao Lv;Yong Wang;Yuanyuan Yang;
      Pages: 839 - 850
      Abstract: Clouds are a common barrier of satellite optical images and adversely affect applications of remotely sensed optical data sets. The optical thickness of clouds varies spatiotemporally. The thickness can be very thin making the detection of thin clouds difficult. A new cirrus band (Band-9) of Landsat-8 has been added to detect thin clouds. However, the majority of spaceborne optical sensors existed previously or in operation do not have the cirrus band. An algorithm is developed to detect thin clouds without using a cirrus band. In particular, the top-of-atmosphere reflectance of thin clouds is modeled using the empirical relationships of the deep blue and blue bands of Landsat-8 Operational Land Imager. A Landsat-8 image of path 14/row 36 near southeastern North Carolina, USA, is used to validate the algorithm. Thin clouds are well-identified when compared to Landsat-8 Band-9 data. The spatial correlation coefficient for both is 93.49%. Therefore, the algorithm is valid. The algorithm is further verified when a blue band and a green band are used to develop the algorithm. Thus, the analytical approach should be extendible to Landsats 4, 5, and 7 sensors or optical sensors as long as they have a blue band and a green band. Finally, the applicability of the algorithm under various atmospheric conditions is verified after analyzing two water vapor absorption spectral bands of NASA/JPL Airborne Visible/Infrared Imaging Spectrometer data.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Hyperspectral Image Classification in the Presence of Noisy Labels
    • Authors: Junjun Jiang;Jiayi Ma;Zheng Wang;Chen Chen;Xianming Liu;
      Pages: 851 - 865
      Abstract: Label information plays an important role in a supervised hyperspectral image classification problem. However, current classification methods all ignore an important and inevitable problem-labels may be corrupted and collecting clean labels for training samples is difficult and often impractical. Therefore, how to learn from the database with noisy labels is a problem of great practical importance. In this paper, we study the influence of label noise on hyperspectral image classification and develop a random label propagation algorithm (RLPA) to cleanse the label noise. The key idea of RLPA is to exploit knowledge (e.g., the superpixel-based spectral-spatial constraints) from the observed hyperspectral images and apply it to the process of label propagation. Specifically, the RLPA first constructs a spectral-spatial probability transform matrix (SSPTM) that simultaneously considers the spectral similarity and superpixel-based spatial information. It then randomly chooses some training samples as “clean” samples and sets the rest as unlabeled samples, and propagates the label information from the “clean” samples to the rest unlabeled samples with the SSPTM. By repeating the random assignment (of “clean” labeled samples and unlabeled samples) and propagation, we can obtain multiple labels for each training sample. Therefore, the final propagated label can be calculated by a majority vote algorithm. Experimental studies show that the RLPA can reduce the level of noisy label and demonstrates the advantages of our proposed method over four major classifiers with a significant margin-the gains in terms of the average overall accuracy, average accuracy, and kappa are impressive, e.g., 9.18%, 9.58%, and 0.1043. The MATLAB source code is available at https://github.com/junjun-jiang/RLPA.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Intracluster Structured Low-Rank Matrix Analysis Method for Hyperspectral
           Denoising
    • Authors: Wei Wei;Lei Zhang;Yining Jiao;Chunna Tian;Cong Wang;Yanning Zhang;
      Pages: 866 - 880
      Abstract: Hyperspectral images (HSIs) denoising aims at eliminating the noise generated during the acquisition and transmission of HSIs. Since denoising is an ill-posed problem, utilizing proper knowledge of HSIs as regularization is essential for a good denoiser. Many HSI denoising methods have been proposed to leverage various prior knowledge, e.g., total variation, sparsity, and so on. Among those knowledge, a low-rank property has been shown to be effective for HSI denoising since it has the ability to deal with the missing values. However, most existing low-rank methods seldom consider mining the useful structures inside the low-rank matrix for a better denoising result. In addition, the rank number needs to be assigned manually. To address these problems, we propose an intracluster structured low-rank matrix analysis method for HSI denoising. First, we divide the original HSI into some clusters by taking advantages of both local similarity and nonlocal similarity structures, with which the resulted clusters are simpler and show more obvious low-rank property. Second, with singular value decomposition on the low-rank matrix in each cluster, the structured sparsity is modeled among the singular values to capture the structure of the low-rank matrix. Finally, an efficient optimization method is proposed to learn the structured sparsity adaptively from the data, as well as to inversely estimate the latent clean HSI from the noisy counterpart. The proposed method can not only obtain better denoising results compared with the-state-of-the-art methods but also automatically determine the rank number. Extensive experimental results demonstrate the effectiveness of the proposed method.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Ionosphere Sensing With a Low-Cost, Single-Frequency, Multi-GNSS Receiver
    • Authors: Chuanbao Zhao;Yunbin Yuan;Baocheng Zhang;Min Li;
      Pages: 881 - 892
      Abstract: The global navigation satellite system (GNSS) data are beneficial for sensing the earth's ionosphere by virtue of their temporal continuity and spatial coverage. In recent years, GNSS (GPS, GLONASS, BDS, and GALILEO) has developed rapidly, with BDS and GALILEO offering position, navigation, and timing services and the ongoing modernization of GPS and GLONASS. The customary approach to sensing the ionosphere normally uses the dual- or multifrequency (DF) data provided by geodetic-grade receivers and consists of two sequential steps. The first step is retrieving ionospheric observables using the carrier-to-code leveling technique. In the second step, using the thin-layer ionospheric model, one can isolate the main vertical total electron content (VTEC) for ionosphere sensing as well as the by-product of the satellite differential code biases (SDCBs) and the receiver differential code biases. In this paper, we proposed a multi-GNSS single-frequency (SF) precise point positioning approach enabling the simultaneous retrieval of VTEC and SDCBs with low-cost receivers. The root mean square of the VTEC differences between the values retrieved from the multi-GNSS SF method and DF method is approximately 0.5 total electron content unit in each system.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Super-Resolution of 3-D GPR Signals to Estimate Thin Asphalt Overlay
           Thickness Using the XCMP Method
    • Authors: Shan Zhao;Imad L. Al-Qadi;
      Pages: 893 - 901
      Abstract: The extended common midpoint (XCMP) method can be used on multichannel 3-D ground-penetrating radar (GPR) to estimate the asphalt pavement thickness and dielectric constant without the need for calibration by taking cores. The XCMP method requires accurate time delay determination of pavement reflection. However, for thin asphalt overlay, the range resolution of 3-D GPR signal is insufficient to resolve the overlapped pulses of asphalt concrete (AC). The objective of this paper is to use multiple signal classification (MUSIC) algorithm to increase the resolution of 3-D GPR signals, such that thin asphalt overlay thickness can be accurately estimated. An evaluation of the MUSIC algorithm at a full-scale test section and a comparison with regularized deconvolution algorithm showed the MUSIC algorithm is an effective approach for increasing the 3-D GPR signal range resolution when the XCMP method is applied on thin AC overlay.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Applying Upstream Satellite Signals and a 2-D Error Minimization Algorithm
           to Advance Early Warning and Management of Flood Water Levels and River
           Discharge
    • Authors: Amir Hossein Zaji;Hossein Bonakdari;Bahram Gharabaghi;
      Pages: 902 - 910
      Abstract: Recent studies demonstrate the power of applying satellite imagery in combination with artificial intelligence (AI) methods to advance the accuracy of forecasting ungauged river network water levels and discharge for early flood warning and management. In predicting river water levels and discharge time series, one of the most common sources of error with AI forecasting algorithms is the input imitation defect. When the input imitation defect occurs, regression methods simply present the input variables as output. In this paper, the input imitation defect is minimized by first introducing the two concepts of vertical error and horizontal error. Subsequently, upstream imagery information is combined with previous lags to propose a new procedure for predicting future satellite signals accurately and with the lowest possible input imitation defect. To accomplish this, the brightness temperature received by the Advanced Microwave Scanning Radiometer is used as a proxy of river discharge. The proposed method (PM) is finally compared with the simple linear regression and three well-known AI methods, i.e., multilayer perceptron, extreme learning machines, and radial basis function. The study outcome indicates that the PM results are more trustworthy and realistic.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Locality and Structure Regularized Low Rank Representation for
           Hyperspectral Image Classification
    • Authors: Qi Wang;Xiang He;Xuelong Li;
      Pages: 911 - 923
      Abstract: Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low-rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying low-dimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, the LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality- and structure-regularized LRR (LSLRR) model is proposed for HSI classification. To overcome the above-mentioned limitations, we present locality constraint criterion and structure preserving strategy to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. In addition, we propose a structural constraint to make the representation have a near-block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI data sets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional
           Neural Network for Change Detection in Multispectral Imagery
    • Authors: Lichao Mou;Lorenzo Bruzzone;Xiao Xiang Zhu;
      Pages: 924 - 935
      Abstract: Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral-spatial-temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network and a recurrent neural network into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependence in bitemporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) it is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; and 3) it is capable of adaptively learning the temporal dependence between multitemporal images, unlike most of the algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed mode.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • The Algorithm for Retrieving the Surface Waves Parameters Using Doppler
           Spectrum Measurements at Small Incident Angles
    • Authors: Yuriy Titchenko;Vladimir Karaev;
      Pages: 936 - 943
      Abstract: In this paper, we present an algorithm for retrieving all the second statistical moments of the sea surface that affect the scattering of waves at small angles of incidence. A feature of this algorithm is the analysis of the spectral characteristics of the reflected waves only, without taking into account the backscattering cross section. The analysis of the width and the shift of the Doppler spectrum (DS) allows us to use the obtained algorithm without the preliminary instrumental calibration, which is necessary for the backscattering cross section. We give final expressions for calculating the width and the shift of the DS in the monostatic problem statement for small angles of incidence. We obtain the expressions within the framework of the Kirchhoff approximation, which makes them reliable only in the quasi-specular reflection region. Features of these formulas are the description of the surface by five statistical moments of the second order and taking into account the anisotropic antenna pattern (AP). To solve the inverse problem of retrieving sea surface parameters, we propose a measurement scheme including three motionless transceiver antennas with different APs. For this measurement scheme, we give analytical expressions for all five unknown parameters of surface waves. Also, we give a theoretical analysis of the accuracy of the algorithm obtained for different wind speeds and wind directions. All the results are valid for the scattering of both acoustic and electromagnetic waves.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Modeling Discrete Forest Anisotropic Reflectance Over a Sloped Surface
           With an Extended GOMS and SAIL Model
    • Authors: Shengbiao Wu;Jianguang Wen;Xingwen Lin;Dalei Hao;Dongqin You;Qing Xiao;Qinhuo Liu;Tiangang Yin;
      Pages: 944 - 957
      Abstract: Topographic effects on canopy reflectance play a pivotal role in the retrieval of surface biophysical variables over rugged terrain. In this paper, we proposed a new canopy anisotropic reflectance model for discrete forests, Geometric Optical and Mutual Shadowing and Scattering-from-Arbitrarily-Inclined-Leaves model coupled with Topography (GOSAILT), which considers the effects of slope, aspect, geotropic nature of tree growth, multiple scattering, and diffuse skylight. GOSAILT-simulated areal proportions of four scene components (i.e., sunlit crown, shaded crown, sunlit background, and shaded background) were evaluated using the Geometric Optical model for Sloping Terrains (GOST) model. The canopy reflectances simulated by GOSAILT were validated against two reflectance data sets: Discrete anisotropic radiative transfer (DART) simulations and wide-angle infrared dual-model line/area array scanner (WIDAS) observations. Compared with a horizontal surface, the forest canopy reflectance over a steep slope (60°) is significantly distorted with absolute (relative) bias values of 0.048 (79.60%) and 0.056 (12.02%) for the red and near-infrared (NIR) bands, respectively. The GOSAILT-simulated component areal proportions show close agreements with GOST. Moreover, GOSAILT simulations have high overall accuracy (red band: coefficient of determination (R2) = 0.96; root-mean-square error (RMSE) = 0.003; and mean absolute percentage error (MAPE) = 3.91%; and NIR band: R2 = 0.78, RMSE = 0.019; MAPE = 3.94%) when compared with the DART simulations. These extensive validations indicate good performances of GOSAILT in canopy reflectance simulations over sloped surfaces.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • $mu$+ m+Reflectance+With+an+Airborne+Laser+Absorption+Spectrometer&rft.title=Geoscience+and+Remote+Sensing,+IEEE+Transactions+on&rft.issn=0196-2892&rft.date=2019&rft.volume=57&rft.spage=958&rft.epage=971&rft.aulast=Spiers;&rft.aufirst=Joseph&rft.au=Joseph+C.+Jacob;Robert+T.+Menzies;Gary+D.+Spiers;">Data Processing and Analysis Approach to Retrieve Carbon Dioxide
           Weighted-Column Mixing Ratio and 2- $mu$ m Reflectance With an Airborne
           Laser Absorption Spectrometer
    • Authors: Joseph C. Jacob;Robert T. Menzies;Gary D. Spiers;
      Pages: 958 - 971
      Abstract: We describe the data processing and analysis algorithms used for high-precision retrievals of CO2 weighted-column mixing ratio and 2-μm surface reflectance from the Carbon Dioxide Laser Absorption Spectrometer (CO2LAS). The CO2LAS at the Jet Propulsion Laboratory, Pasadena, CA, USA, is one of the instruments designed to demonstrate capabilities needed for the NASA Active Sensing over Nights, Days, and Seasons mission concept. The integrated path differential absorption technique is used in CO2 retrieval. The along-track spatial resolution for the airborne measurements described here ranges from 10 m to 1 km. Our approach employs heterodyne detection of two laser signals reflected off earth's surface. Frequency domain processing enables return signal peak detection and high-fidelity power estimation. We compensate for range to ground variations using a digital elevation model and emphasize the importance of reflectance weighting in time averaging of the surface elevation. Quality control filters are applied, as well as a statistical methodology to filter laser speckle fluctuations. Reflectance is also retrieved on the scale of a few meters of ground track. Our data processing workflow and example retrievals are presented.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • High-Resolution Radar Imaging in Complex Environments Based on Bayesian
           Learning With Mixture Models
    • Authors: Xueru Bai;Yu Zhang;Feng Zhou;
      Pages: 972 - 984
      Abstract: We address the problem of high-resolution radar imaging in complex environments in a Bayesian framework. We perform model order selection and sparse weights estimation via the maximum a posterior-expectation maximization technique in a statistical model, where the noise obeys Gaussian mixture distribution; and the weights are governed by the sparsity-promoting Gamma–Gaussian hierarchical prior. The proposed method has closed-form solution and can be implemented efficiently by matrix operation. Experiments has shown that accounting for the noise with Gaussian mixture model instead of single Gaussian greatly improves the performance, and the proposed method provides an effective way of high-resolution radar imaging in complex environments such as barrage jamming and micro-Doppler interference.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Using Artificial Neural Network Ensembles With Crogging Resampling
           Technique to Retrieve Sea Surface Temperature From HY-2A Scanning
           Microwave Radiometer Data
    • Authors: Gang Zheng;Jingsong Yang;Xiaofeng Li;Lizhang Zhou;Lin Ren;Peng Chen;Huaguo Zhang;Xiulin Lou;
      Pages: 985 - 1000
      Abstract: The brightness temperature data acquired during 2012-2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite- Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) 0.25° × 0.25° gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Variational Deconvolution of Conically Scanned Passive Microwave
           Observations With Error Quantification
    • Authors: Jeffrey Steward;Ziad Haddad;Svetla Hristova-Veleva;Sahra Kacimi;Eun-Kyoung Seo;
      Pages: 1001 - 1014
      Abstract: The deconvolution of potentially cloud-affected passive microwave brightness temperatures is an important step for utilization in direct data assimilation in cloud-resolving numerical weather prediction (NWP) models for the purpose of improving model initial conditions. Geophysical retrieval algorithms, such as precipitation rate retrievals, also benefit from consistent resolution across channels. In this paper, we explore how to derive the posterior error estimates that are required for ingestion into data assimilation models or end-to-end error-quantified retrieval algorithms. To this end, we present a minimum variance, best linear-unbiased estimator approach that seeks an optimal estimate of the apparent (i.e., without the effects of antenna pattern convolution) brightness temperatures by iteratively minimizing a cost function measuring the lack of fit between observations and departures from a first guess. Both the observation and first-guess departure terms are weighed by a corresponding covariance term that estimates their relative uncertainty. The first-guess uncertainty, a Bayesian prior “belief” in the spread of the first-guess error, is estimated using geophysical fields from an NWP model in a radiative transfer model plus an antenna pattern forward operator, then iteratively improved using the posterior deconvolved brightness temperatures of actual special sensor microwave imager/sounder observations. The error for the posterior distribution, subject to the initial belief, is derived. The error-quantified results are shown to increase the spatial resolution of microwave observations.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Suomi-NPP OMPS Nadir Mapper’s Operational SDR Performance
    • Authors: Chunhui Pan;Lihang Zhou;Changyong Cao;Lawrence Flynn;Eric Beach;
      Pages: 1015 - 1024
      Abstract: The ozone mapping and profiler suite (OMPS) is carried onboard the Suomi National Polar-orbiting Partnership satellite that was launched on October 28, 2011. The OMPS mission objectives concern atmospheric ozone concentrations and their variations in the earth atmosphere. A successful thorough on-orbit sensor calibration enabled current validated operational sensor data record (SDR) stage, providing quality sensor data that meet the requirements and users' expectations. The calibration coefficients derived from this paper have been successfully used in a life-cycle SDR reprocessing to improve sensor data quality. In this paper, we present our qualitative analyses and the results of OMPS nadir mapper SDR on-orbit calibration, and address current SDR data quality in relation to instrument detector performance, stray light correction, and wavelength registration. Our OMPS SDR calibration experience sets a reference for the successor instruments for accurate long-term monitoring of ozone total column concentrations.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • SAR Processing Without a Motion Measurement System
    • Authors: Jan Torgrimsson;Patrik Dammert;Hans Hellsten;Lars M. H. Ulander;
      Pages: 1025 - 1039
      Abstract: This paper leads a discussion on how to form a Synthetic Aperture Radar (SAR) image without knowing the relative track. That is, within the scope of factorized geometrical autofocus (FGA). The FGA algorithm is a base-2 fast factorized back-projection (FFBP) formulation with six free geometry parameters (per subaperture pair). These are tuned step by step until a sharp image is obtained. This innovative autofocus concept can compensate completely for an erroneous geometry. The FGA algorithm has been applied successfully on two ultrawideband (UWB) data sets, acquired by the CARABAS II system at very high frequency (VHF)-band. The relative tracks are known (measured accurately). We, however, adopt and modify a basic geometry model. A linear equidistant track at fixed altitude is initially assumed. Apart from deviations due to linearization, a ~2.5-m/s along-track velocity error is also introduced. Resulting FGA images are compared to reference images and verified to be focused. This indicates that it is feasible to form a wavelength-resolution SAR image at VHF-band without support from a motion measurement system. The execution time for the examples in this paper is about five times longer with autofocus than without. Hence, the FGA algorithm is now fit for use on a regular basis.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Gradient-Based Automatic Lookup Table Generator for Radiative Transfer
           Models
    • Authors: Jorge Vicent Servera;Luis Alonso;Luca Martino;Neus Sabater;Jochem Verrelst;Gustau Camps-Valls;José Moreno;
      Pages: 1040 - 1048
      Abstract: Physically based radiative transfer models (RTMs) are widely used in Earth observation to understand the radiation processes occurring on the Earth's surface and their interactions with water, vegetation, and atmosphere. Through continuous improvements, RTMs have increased in accuracy and representativity of complex scenes at expenses of an increase in complexity and computation time, making them impractical in various remote sensing applications. To overcome this limitation, the common practice is to precompute large lookup tables (LUTs) for their later interpolation. To further reduce the RTM computation burden and the error in LUT interpolation, we have developed a method to automatically select the minimum and optimal set of input-output points (nodes) to be included in an LUT. We present the gradient-based automatic LUT generator algorithm (GALGA), which relies on the notion of an acquisition function that incorporates: 1) the Jacobian evaluation of an RTM and 2) the information about the multivariate distribution of the current nodes. We illustrate the capabilities of GALGA in the automatic construction and optimization of MODTRAN-based LUTs of different dimensions of the input variables space. Our results indicate that when compared with a pseudorandom homogeneous distribution of the LUT nodes, GALGA reduces: 1) the LUT size by >24%; 2) the computation time by ~27%; and 3) the maximum interpolation relative errors by at least 10%. It is concluded that an automatic LUT design might benefit from the methodology proposed in GALGA to reduce interpolation errors and computation time in computationally expensive RTMs.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • SCoBi-Veg: A Generalized Bistatic Scattering Model of Reflectometry From
           Vegetation for Signals of Opportunity Applications
    • Authors: Mehmet Kurum;Manohar Deshpande;Alicia T. Joseph;Peggy E. O’Neill;Roger H. Lang;Orhan Eroglu;
      Pages: 1049 - 1068
      Abstract: SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • A Modeling-Based Approach for Soil Frost Detection in the Northern Boreal
           Forest Region With C-Band SAR
    • Authors: Juval Cohen;Kimmo Rautiainen;Jaakko Ikonen;Juha Lemmetyinen;Tuomo Smolander;Juho Vehvilêinen;Jouni Pulliainen;
      Pages: 1069 - 1083
      Abstract: This paper presents a new approach for monitoring soil frost in the northern boreal forest region using co-polarized C-band synthetic aperture radar (SAR) data. Due to the high sensitivity of the C-band signal to vegetation, estimating the soil freeze/thaw (F/T) state directly from the measured backscatter is not feasible over dense vegetation, such as boreal forests. The presented method is based on applying a simple zeroth-order model to estimate the contribution of the ground and the forest canopy on the observed total backscatter. The retrieved ground and canopy backscatter values were compared with in situ information on soil F/T state. By using a linear least sum of square errors classification algorithm, the retrieved ground and canopy backscatter values representing frozen and thawed ground were successfully separated. The method was tested for various soil types and incidence angles. For soil types with higher water holding capacities and lower infiltration rates such as fine Haplic Podzol and Umbric Gleysol, the estimation accuracy of the F/T state was over 97%, whereas for drier, well-drained soil types such as Haplic Arenosol and Coarse Haplic Podzol it was over 94%. Estimation accuracy slightly increased with higher incidence angle. The method is not feasible in rocky terrain due to very low water content, or in wet snow conditions due to lack of penetration of the C-band SAR signal through wet snow. With low ancillary data and computational requirements, the proposed method is applicable for continuous near real-time monitoring of soil F/T state.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Parameter Optimization of a Discrete Scattering Model by Integration of
           Global Sensitivity Analysis Using SMAP Active and Passive Observations
    • Authors: Xiaojing Bai;Jiangyuan Zeng;Kun-Shan Chen;Zhen Li;Yijian Zeng;Jun Wen;Xin Wang;Xiaohua Dong;Zhongbo Su;
      Pages: 1084 - 1099
      Abstract: Active and passive microwave signatures respond differently to the land surface and provide complementary information on the characteristics of the observed scenes. The objective of this paper is to explore the synergy of active radar and passive radiometer observations at the same spatial scale to constrain a discrete radiative transfer model, the Tor Vergata (TVG) model, to gain insights into the microwave scattering and emission mechanisms over grasslands. The TVG model can simultaneously simulate the backscattering coefficient and emissivity with a set of input parameters. To calibrate this model, in situ soil moisture and temperature data collected from the Maqu area in the northeastern region of the Tibetan Plateau, interpolated leaf area index (LA!) data from the Moderate Resolution Imaging Spectroradiometer LAI eight-day products, and concurrent and coincident Soil Moisture Active Passive (SMAP) radar and radiometer observations are used. Because this model needs numerous input parameters to be driven, the extended Fourier amplitude sensitivity test is first applied to conduct global sensitivity analysis (GSA) to select the sensitive and insensitive parameters. Only the most sensitive parameters are defined as free variables, to separately calibrate the activeonly model (TVG-A), the passive-only model (TVG-P), and the active and passive combined model (TVG-AP). The accuracy of the calibrated models is evaluated by comparing the SMAP observations and the model simulations. The results show that TVG-AP can well reproduce the backscattering coefficient and brightness temperature, with correlation coefficients of 0.87, 0.89, 0.78, and 0.43 and root-mean-square errors of 0.49 dB, 0.52 dB, 7.20 K, and 10.47 K for σHHo, σVVo, TBH, and TBV, respectively. In contrast, TVG-A and TVG-P can only accurately model the backscattering coefficient and brightness temperatu-e, respectively. Without any modifications of the calibrated parameters, the error metrics computed from the validation data are slightly worse than those of the calibration data. These results demonstrate the feasibility of the synergistic use of SMAP active radar and passive radiometer observations under the unified framework of a physical model. In addition, the results demonstrate the necessity and effectiveness of applying GSA in model optimization. It is expected that these findings can contribute to the development of model-based soil moisture retrieval methods using active and passive microwave remote sensing data.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Buildings Detection in VHR SAR Images Using Fully Convolution Neural
           Networks
    • Authors: Muhammad Shahzad;Michael Maurer;Friedrich Fraundorfer;Yuanyuan Wang;Xiao Xiang Zhu;
      Pages: 1100 - 1116
      Abstract: This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high-resolution (VHR) synthetic aperture radar (SAR) images. In this context, this paper has two major contributions. First, it presents a novel and generic workflow that initially classifies the spaceborne SAR tomography (TomoSAR) point clouds-generated by processing VHR SAR image stacks using advanced interferometric techniques known as TomoSAR-into buildings and nonbuildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labeled (buildings/nonbuildings) SAR data sets. Second, these labeled data sets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep fully convolution neural networks with an additional conditional random field represented as a recurrent neural network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km 2-almost the whole city of Berlin- with the mean pixel accuracies of around 93.84%.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Convolution Structure Sparse Coding for Fusion of Panchromatic and
           Multispectral Images
    • Authors: Kai Zhang;Min Wang;Shuyuan Yang;Licheng Jiao;
      Pages: 1117 - 1130
      Abstract: Recently, sparse coding-based image fusion methods have been developed extensively. Although most of them can produce competitive fusion results, three issues need to be addressed: 1) these methods divide the image into overlapped patches and process them independently, which ignore the consistency of pixels in overlapped patches; 2) the partition strategy results in the loss of spatial structures for the entire image; and 3) the correlation in the bands of multispectral (MS) image is ignored. In this paper, we propose a novel image fusion method based on convolution structure sparse coding (CSSC) to deal with these issues. First, the proposed method combines convolution sparse coding with the degradation relationship of MS and panchromatic (PAN) images to establish a restoration model. Then, CSSC is elaborated to depict the correlation in the MS bands by introducing structural sparsity. Finally, feature maps over the constructed high-spatial-resolution (HR) and low-spatial-resolution (LR) filters are computed by alternative optimization to reconstruct the fused images. Besides, a joint HR/LR filter learning framework is also described in detail to ensure consistency and compatibility of HR/LR filters. Owing to the direct convolution on the entire image, the proposed CSSC fusion method avoids the partition of the image, which can efficiently exploit the global correlation and preserve the spatial structures in the image. The experimental results on QuickBird and Geoeye-1 satellite images show that the proposed method can produce better results by visual and numerical evaluation when compared with several well-known fusion methods.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Enhanced 1-Bit Radar Imaging by Exploiting Two-Level Block Sparsity
    • Authors: Xueqian Wang;Gang Li;Yu Liu;Moeness G. Amin;
      Pages: 1131 - 1141
      Abstract: Conventional compressive sensing (CS) aims at sparse signal recovery from the measurements with continuous values. Quantized CS (QCS) methods arise in digital implementations where quantization of the receiver data is performed prior to signal processing. The extreme case of QCS is the so-called 1-bit CS where each real-valued measurement maintains only the sign information with one bit. The 1-bit CS alleviates the burden of storage and transmission of large data volumes and reduces the cost of the analog-to-digital converter. Recently, the 1-bit CS has been successfully applied to inverse scattering and radar imaging. In high-resolution radar imaging scenarios, targets assume spatial extent and occupy clustering pixels. The real and imaginary components of a complex sparse signal are the projections of the same complex value onto two orthogonal axes and, therefore, share a joint sparsity pattern. In this paper, a new 1-bit CS algorithm, referred to as enhanced-binary iterative hard thresholding (E-BIHT), is proposed to improve quality of 1-bit radar imaging by exploiting the two-level block sparsity exhibited in the two properties of clustering and the joint sparsity pattern of the real and imaginary parts of the target image. Simulations and experimental results demonstrate that compared to commonly used 1-bit CS algorithms, the proposed E-BIHT provides more informative imaging resulting in higher target-to-clutter ratio.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Spectral–Spatial Gabor Surface Feature Fusion Approach for Hyperspectral
           Imagery Classification
    • Authors: Sen Jia;Kuilin Wu;Jiasong Zhu;Xiuping Jia;
      Pages: 1142 - 1154
      Abstract: Since the spatial distribution of surface materials is usually regular and locally continuous, it is reasonable to utilize the spectral and spatial information for the hyperspectral image classification. In this paper, a spectral–spatial Gabor surface feature (GSF) fusion approach has been proposed for hyperspectral image classification. First, Gabor magnitude pictures (GMPs) are extracted by applying a set of predefined 2-D Gabor filters to hyperspectral images. Second, the GSF has been extended to the spectral–spatial domains to comply with the 3-D structure of hyperspectral imagery, called 3-DGSF, which utilizes the first-order derivative of GMPs. Meanwhile, a classic superpixel segmentation method, called simple linear iterative clustering (SLIC), is adopted to divide the original hyperspectral image into disjoint superpixels. Third, principal component analysis is adopted to reduce the dimensionality of each extracted 3-DGSF feature cube. Next, a support vector machine classifier is applied on each reduced 3-DGSF features, and the majority voting strategy is used to obtain the classification results. Finally, the superpixel map obtained by SLIC is used to regularize the classification map, and thus, the proposed approach is named as S3-DGSF. Extensive experiments on three real hyperspectral data sets have demonstrated the higher performance of the proposed S3-DGSF approach over several state-of-the-art methods in the literature.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Scene Classification With Recurrent Attention of VHR Remote Sensing Images
    • Authors: Qi Wang;Shaoteng Liu;Jocelyn Chanussot;Xuelong Li;
      Pages: 1155 - 1167
      Abstract: Scene classification of remote sensing images has drawn great attention because of its wide applications. In this paper, with the guidance of the human visual system (HVS), we explore the attention mechanism and propose a novel end-to-end attention recurrent convolutional network (ARCNet) for scene classification. It can learn to focus selectively on some key regions or locations and just process them at high-level features, thereby discarding the noncritical information and promoting the classification performance. The contributions of this paper are threefold. First, we design a novel recurrent attention structure to squeeze high-level semantic and spatial features into several simplex vectors for the reduction of learning parameters. Second, an end-to-end network named ARCNet is proposed to adaptively select a series of attention regions and then to generate powerful predictions by learning to process them sequentially. Third, we construct a new data set named OPTIMAL-31, which contains more categories than popular data sets and gives researchers an extra platform to validate their algorithms. The experimental results demonstrate that our model makes great promotion in comparison with the state-of-the-art approaches.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • A Local Projection-Based Approach to Individual Tree Detection and 3-D
           Crown Delineation in Multistoried Coniferous Forests Using High-Density
           Airborne LiDAR Data
    • Authors: Aravind Harikumar;Francesca Bovolo;Lorenzo Bruzzone;
      Pages: 1168 - 1182
      Abstract: Accurate crown detection and delineation of dominant and subdominant trees are crucial for accurate inventorying of forests at the individual tree level. The state-of-the-art tree detection and crown delineation methods have good performance mostly with dominant trees, whereas exhibits a reduced accuracy when dealing with subdominant trees. In this paper, we propose a novel approach to accurately detect and delineate both the dominant and subdominant tree crowns in conifer-dominated multistoried forests using small footprint high-density airborne Light Detection and Ranging data. Here, 3-D candidate cloud segments delineated using a canopy height model segmentation technique are projected onto a novel 3-D space where both the dominant and subdominant tree crowns can be accurately detected and delineated. Tree crowns are detected using 2-D features derived from the projected data. The delineation of the crown is performed at the voxel level with the help of both the 2-D features and 3-D texture information derived from the cloud segment. The texture information is modeled by using 3-D Gray Level Co-occurrence Matrix. The performance evaluation was done on a set of six circular plots for which reference data are available. The high detection and delineation accuracies obtained over the state of the art prove the performance of the proposed method.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Self-Paced Joint Sparse Representation for the Classification of
           Hyperspectral Images
    • Authors: Jiangtao Peng;Weiwei Sun;Qian Du;
      Pages: 1183 - 1194
      Abstract: In this paper, a self-paced joint sparse representation (SPJSR) model is proposed for the classification of hyperspectral images (HSIs). It replaces the least-squares (LS) loss in the standard joint sparse representation (JSR) model with a weighted LS loss and adopts a self-paced learning (SPL) strategy to learn the weights for neighboring pixels. Rather than predefining a weight vector in the existing weighted JSR methods, both the weight and sparse representation (SR) coefficient associated with neighboring pixels are optimized by an alternating iterative strategy. According to the nature of SPL, in each iteration, neighboring pixels with nonzero weights (i.e., easy pixels) are included for the joint SR of a testing pixel. With the increase of iterations, the model size (i.e., the number of selected neighboring pixels) is enlarged and more neighboring pixels from easy to complex are gradually added into the JSR learning process. After several iterations, the algorithm can be terminated to produce a desirable model that includes easy homogeneous pixels and excludes complex inhomogeneous pixels. Experimental results on two benchmark hyperspectral data sets demonstrate that our proposed SPJSR is more accurate and robust than existing JSR methods, especially in the case of heavy noise.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Snowpack Monitoring Using a Dual-Receiver Radar Architecture
    • Authors: Marco Pasian;Massimiliano Barbolini;Fabio Dell’Acqua;Pedro Fidel Espín-López;Lorenzo Silvestri;
      Pages: 1195 - 1204
      Abstract: Risk mitigation strategies to reduce the impact of avalanches on infrastructures, such as evacuation of mountain villages, and planned closure of roads, railways and ski resorts, are heavily dependent on avalanche forecasting capability. Moreover, the possibility to determine the snow water equivalent (SWE) of a snowpack is a crucial step for water management strategies used, for example, in agriculture and hydroelectric power plants. In both cases, for dry snow, two key physical parameters are the total snow thickness and the wave speed in the medium. Microwave radars are being used to monitor snowpacks, but they invariably invoke external aids or a priori assumptions to calculate these physical parameters. This paper presents an innovative radar architecture for snowpack monitoring, of a single emitting and two receiving antennas. This novel configuration enables simultaneous identification of both total snow thickness and wave speed in the medium without any additional hypothesis or device. For dry snow, consequently, snow density and SWE can also be immediately determined. The proposed architecture is validated using first numerical simulations and then indoor and outdoor experimental results. These latter achieved accuracy levels better than 10% for total snow thickness and better than 13% for wave speed.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual
           Convolutional Neural Network
    • Authors: Qiangqiang Yuan;Qiang Zhang;Jie Li;Huanfeng Shen;Liangpei Zhang;
      Pages: 1205 - 1218
      Abstract: Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel deep learning-based method for this task is proposed, by learning a nonlinear end-to-end mapping between the noisy and clean HSIs with a combined spatial-spectral deep convolutional neural network (HSID-CNN). Both the spatial and spectral information are simultaneously assigned to the proposed network. In addition, multiscale feature extraction and multilevel feature representation are, respectively, employed to capture both the multiscale spatial-spectral feature and fuse different feature representations for the final restoration. The simulated and real-data experiments demonstrate that the proposed HSID-CNN outperforms many of the mainstream methods in both the quantitative evaluation indexes, visual effects, and HSI classification accuracy.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Comparing Coincident Elevation and Freeboard From IceBridge and Five
           Different CryoSat-2 Retrackers
    • Authors: Donghui Yi;Nathan Kurtz;Jeremy Harbeck;Ron Kwok;Stefan Hendricks;Robert Ricker;
      Pages: 1219 - 1229
      Abstract: The airborne Operation IceBridge and spaceborne CryoSat-2 missions observe polar sea ice at different spatial and temporal scales as well as with different sensor suites. Comparison of data products from IceBridge and CryoSat-2 is complicated by the fact that they use different geophysical corrections: reference ellipsoid, geoid model, tide model, and atmospheric corrections to derive surface elevation and sea-ice freeboard. In this paper, we compare sea-ice surface elevation and freeboard using eight coincident CryoSat-2, Airborne Topographic Mapper (ATM), and Land, Vegetation, and Ice Sensor (LVIS) observations with direct IceBridge underflights of CryoSat-2 ground tracks. We apply identical geophysical corrections to CryoSat-2 and IceBridge data to eliminate elevation biases due to these differences and focus on differences due to retracker performance. The IceBridge ATM and LVIS elevation and freeboard and Snow Radar snow depth data sets are averaged to each CryoSat-2 footprint for comparison. With snow depth measurements, we are able to compare elevations and freeboards at the snow/ice interface for five different CryoSat-2 retrackers (ESA, GSFCv1, AWI, JPL, and GSFCv2) and IceBridge. The overall mean of freeboard differences between GSFCv2, ESA, AWI, JPL retrackers, and ATM are in agreement within 0.05 m. However, the five different CryoSat-2 retrackers show distinct differences in mean elevation over leads and over floes. This suggests that the physical interpretation of the different retrackers needs to be considered depending on usage, for example, elevations from CryoSat-2 retrackers need to be carefully calibrated before comparing with elevation from other satellites for long-term surface elevation trends.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
  • Kernel Collaborative Representation With Local Correlation Features for
           Hyperspectral Image Classification
    • Authors: Hongjun Su;Bo Zhao;Qian Du;Peijun Du;
      Pages: 1230 - 1241
      Abstract: Spatial information has widely been used in hyperspectral image (HSI) classification to improve classification accuracy. However, the structural information may not be fully explored when using spatial information, this paper proposes the joint collaborative representation classification with correlation matrix (CRC-CM) for HSI by using spatial correlation features in patches, which could keep the local intrinsic structure in band images. Considering spatial heterogeneity in a patch, local correlation matrices of a target neighborhood patch and training neighborhood patch are improved by a binary weight matrix and shape-adaptive neighborhood. To explore nonlinear nature of spatial features, corresponding kernel CRC-CM is also proposed. To evaluate the effectiveness of the proposed methods, three real HSIs with different degree of heterogeneity are used. The experimental results show that the proposed spatial correlation features outperform the original spectral feature and other spatial features which widely used in HSI classifiers.
      PubDate: Feb. 2019
      Issue No: Vol. 57, No. 2 (2019)
       
 
 
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