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  Subjects -> ELECTRONICS (Total: 207 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advanced Materials Technologies     Hybrid Journal   (Followers: 1)
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 8)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 9)
Advances in Electronics     Open Access   (Followers: 100)
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: 40)
Advancing Microelectronics     Hybrid Journal  
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 28)
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: 16)
Australian Journal of Electrical and Electronics Engineering     Hybrid Journal  
Batteries     Open Access   (Followers: 9)
Batteries & Supercaps     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 31)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 2)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 9)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 308)
ECTI Transactions on Computer and Information Technology (ECTI-CIT)     Open Access  
ECTI Transactions on Electrical Engineering, Electronics, and Communications     Open Access   (Followers: 2)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 124)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 109)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 103)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elektronika ir Elektortechnika     Open Access   (Followers: 2)
Elkha : Jurnal Teknik Elektro     Open Access  
Emitor : Jurnal Teknik Elektro     Open Access   (Followers: 2)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage     Hybrid Journal   (Followers: 1)
Energy Storage Materials     Full-text available via subscription   (Followers: 4)
EPE Journal : European Power Electronics and Drives     Hybrid Journal  
EPJ Quantum Technology     Open Access   (Followers: 1)
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
IACR Transactions on Symmetric Cryptology     Open Access   (Followers: 1)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 101)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 82)
IEEE Embedded Systems Letters     Hybrid Journal   (Followers: 57)
IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology     Hybrid Journal   (Followers: 3)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 52)
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 Letters on Electromagnetic Compatibility Practice and Applications     Hybrid Journal   (Followers: 4)
IEEE Magnetics Letters     Hybrid Journal   (Followers: 7)
IEEE Nanotechnology Magazine     Hybrid Journal   (Followers: 42)
IEEE Open Journal of Circuits and Systems     Open Access   (Followers: 3)
IEEE Open Journal of Industry Applications     Open Access   (Followers: 3)
IEEE Open Journal of the Industrial Electronics Society     Open Access   (Followers: 3)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 77)
IEEE Pulse     Hybrid Journal   (Followers: 5)
IEEE Reviews in Biomedical Engineering     Hybrid Journal   (Followers: 23)
IEEE Solid-State Circuits Letters     Hybrid Journal   (Followers: 3)
IEEE Solid-State Circuits Magazine     Hybrid Journal   (Followers: 13)
IEEE Transactions on Aerospace and Electronic Systems     Hybrid Journal   (Followers: 367)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 74)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 62)
IEEE Transactions on Autonomous Mental Development     Hybrid Journal   (Followers: 8)
IEEE Transactions on Biomedical Engineering     Hybrid Journal   (Followers: 39)
IEEE Transactions on Broadcasting     Hybrid Journal   (Followers: 13)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 26)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 46)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Geoscience and Remote Sensing     Hybrid Journal   (Followers: 226)
IEEE Transactions on Haptics     Hybrid Journal   (Followers: 5)
IEEE Transactions on Industrial Electronics     Hybrid Journal   (Followers: 75)
IEEE Transactions on Industry Applications     Hybrid Journal   (Followers: 40)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 27)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 80)
IEEE Transactions on Services Computing     Hybrid Journal   (Followers: 4)
IEEE Transactions on Signal and Information Processing over Networks     Hybrid Journal   (Followers: 14)
IEEE Transactions on Software Engineering     Hybrid Journal   (Followers: 79)
IEEE Women in Engineering Magazine     Hybrid Journal   (Followers: 11)
IEEE/OSA Journal of Optical Communications and Networking     Hybrid Journal   (Followers: 16)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Energy Systems Integration     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 36)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 60)
IET Smart Grid     Open Access   (Followers: 1)
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 Technology Research Journal Phranakhon Rajabhat University     Open Access  
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: 14)
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: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 5)
International Journal of Control     Hybrid Journal   (Followers: 11)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 3)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Hybrid Intelligence     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 16)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 10)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 25)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 12)
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: 38)
Journal of Electrical Bioimpedance     Open Access  
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 8)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 9)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronic Science and Technology     Open Access   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 5)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 4)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 187)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 10)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 10)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal   (Followers: 1)
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 11)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 32)
Journal of Power Electronics     Hybrid Journal   (Followers: 2)
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: 27)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 6)
Microelectronics and Solid State Electronics     Open Access   (Followers: 28)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 9)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 2)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 4)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 11)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 6)
Revue Méditerranéenne des Télécommunications     Open Access  
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 57)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Solid State Electronics Letters     Open Access  
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 3)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Transactions on Cryptographic Hardware and Embedded Systems     Open Access   (Followers: 2)

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Similar Journals
Journal Cover
IEEE Transactions on Geoscience and Remote Sensing
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 226  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892 - ISSN (Online) 1558-0644
Published by IEEE Homepage  [229 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: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • 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: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Presents the GRSS society institutional listings.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Polarimetric Bistatic Scattering From Multiple Parallel Cylinders
    • Authors: Chao Yang;Yang Du;
      Pages: 3742 - 3753
      Abstract: Electromagnetic scattering from multiple cylinders has been an active research topic in many research fields, and the special case of parallel cylinders has seen important applications ranging from remote sensing to biology. Yet, the rigorous treatments have been mostly focusing on the 2-D cases, whereas for the more realistic 3-D cases, approximated approaches are often adopted. This article proposes a more rigorous treatment of 3-D polarimetric multiple scattering from parallel cylinders by extending our previously proposed virtual partition method (VPM) for single-cylinder case to multiple-cylinder case. The appeal of the method includes the appreciably reduced longitudinal dimension for subcylinders and its corresponding encircling sphere so as to effectively address the otherwise thorny issue of violation of the required mutual exclusion of encircling spheres of large aspect ratio cylinders by the conventional multiple scatterers’ equation. The proposed VPM demonstrates the capability of capturing very well the polarimetric bistatic scattering amplitudes and phases, and of meeting physical requirements of energy conservation and reciprocity theorem. A systematic examination of the coupling effect is carried out against a number of factors, including average interneighbor-cylinder (intercylinder for short) distance, cylinder size, dielectric constant, and cylinder number, with the numerical results clearly revealing the complicated pattern of coupling effect. The studied cases suggest that the coupling effect may still be felt for a large average intercylinder distance. For electromagnetic wave propagation in the parallel cylinders, the coupling effect is visible, yet it tends to be mitigated by the average process.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Digital Terrain, Surface, and Canopy Height Models From InSAR
           Backscatter-Height Histograms
    • Authors: Gustavo H. X. Shiroma;Marco Lavalle;
      Pages: 3754 - 3777
      Abstract: This article demonstrates how 3-D vegetation structure can be approximated by interferometric synthetic aperture radar (InSAR) backscatter-height histograms. Single-look backscatter measurements are plotted against the InSAR phase height and are aggregated spatially over a forest patch to form a 3-D histogram, referred to as InSAR backscatter-height histogram or simply InSAR histogram. InSAR histograms resemble LiDAR waveforms, suggesting that existing algorithms used to retrieve canopy height and ground topography from radar tomograms or LiDAR waveforms can be applied to InSAR histograms. Three algorithms are evaluated to generate maps of digital terrain, surface, and canopy height models: Gaussian decomposition, quantile, and backscatter threshold. Full-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) data collected over the Gabonese Lopé National Park during the 2016 AfriSAR campaign are used to illustrate and compare the performance of the algorithms for the HH, HV, VV, HH+VV, and HH−VV polarimetric channels. Results show that radar-derived maps using the InSAR histograms differ by 4 m (top-canopy), 5 m (terrain), and 6 m (forest height) in terms of average root-mean-square errors (RMSEs) from standard maps derived from full-waveform laser, vegetation, and ice sensor (LVIS) LiDAR measurements.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Ensemble Learning for Hyperspectral Image Classification Using Tangent
           Collaborative Representation
    • Authors: Hongjun Su;Yao Yu;Qian Du;Peijun Du;
      Pages: 3778 - 3790
      Abstract: Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor
           for Hyperspectral Image Classification
    • Authors: Danfeng Hong;Xin Wu;Pedram Ghamisi;Jocelyn Chanussot;Naoto Yokoya;Xiao Xiang Zhu;
      Pages: 3791 - 3808
      Abstract: So far, a large number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and analysis. However, locally semantic change, such as scene composition, relative position between objects, spectral variability caused by illumination, atmospheric effects, and material mixture, has been less frequently investigated in modeling spatial information. Consequently, identifying the same materials from spatially different scenes or positions can be difficult. In this article, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs). IAPs extract the spatial invariant features by exploiting isotropic filter banks or convolutional kernels on HSI and spatial aggregation techniques (e.g., superpixel segmentation) in the Cartesian coordinate system. Furthermore, they model invariant behaviors (e.g., shift, rotation) by the means of a continuous histogram of oriented gradients constructed in a Fourier polar coordinate. This yields a combinatorial representation of spatial-frequency invariant features with application to HSI classification. Extensive experiments conducted on three promising hyperspectral data sets (Houston2013 and Houston2018) to demonstrate the superiority and effectiveness of the proposed IAP method in comparison with several state-of-the-art profile-related techniques. The codes will be available from the website: https://sites.google.com/view/danfeng-hong/data-code.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Validation of Sentinel-3A SRAL Coastal Sea Level Data at High Posting
           Rate: 80 Hz
    • Authors: Ana Aldarias;Jesús Gómez-Enri;Irene Laiz;Begoña Tejedor;Stefano Vignudelli;Paolo Cipollini;
      Pages: 3809 - 3821
      Abstract: Altimetry data of two and a half years (June 2016–November 2018) of Sentinel-3A SRAL (S3A-SRAL) were validated at the sampling frequency of 80 Hz. The data were obtained from the European Space Agency (ESA) Grid Processing On Demand (GPOD) service over three coastal sites in Spain: Huelva (HU) (Gulf of Cádiz), Barcelona (BA) (Western Mediterranean Sea), and Bilbao (BI) (Bay of Biscay). Two tracks were selected in each site: one ascending and one descending. Data were validated using in situ tide gauge (TG) data provided by the Spanish Puertos del Estado. The altimetry sea level anomaly time series were obtained using the corrections available in GPOD with the exception of the sea state bias (SSB) correction, not available at 80 Hz. Hence, the SSB was approximated to 5% of the significant wave height (SWH). The validation was performed using two statistical parameters, the Pearson correlation coefficient (r) and the root mean square error (rmse). In the 5–20-km segment with respect to the coastline, the results were 6–8 cm (rmse) and 0.7–0.8 (r) for all the tracks. The 0–5-km segment was also analyzed in detail to study the land effect on the altimetry data quality. The results showed that the track orientation, the angle of intersection with the coast, and the land topography concur to determine the nearest distance to the coast at which the data retain a similar level of accuracy than in the 5–20-km segment. This “distance of good quality” to shore reaches a minimum of 3 km for the tracks at HU and the descending track at BA.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Preliminary Results of Multichannel SAR-GMTI Experiments for Airborne
           Quad-Pol Radar System
    • Authors: Zhiwei Yang;Huajian Xu;Penghui Huang;Aifang Liu;Min Tian;Guisheng Liao;
      Pages: 3822 - 3840
      Abstract: Much research from open literature shows that polarization diversity can provide another dimension which may be exploited to improve the performance in ground moving target indication (GMTI), compared with space–time adaptive processing (STAP). In this article, we report the multichannel synthetic aperture radar (SAR)-based GMTI (SAR-GMTI) experiment and its preliminary results with a N-SAR system which is an airborne quadrature-polarimetric (quad-pol) radar system. First, the joint polarization-space adaptive processing (JPolSAP) is performed in the image level, but two suboptimal versions of JPolSAP, where the polarimetric matched filter (PMF) vector and the full-one vector are exploited to substitute for the polarimetric steering vector, respectively, are evaluated since the polarimetric steering vector of the moving target is unknown precisely in practical applications. Then, considering the computational complexity and lack of secondary data in a inhomogeneous environment due to high degrees of freedom of the JPolSAP processor, two cascade processors are evaluated, including the polarization enhancement that uses PMF and noncoherence integration detection (NCID) technique. Furthermore, we utilize the polarization information to accomplish SAR terrain classification, and subsequently secondary data from the same scattering type clutter can be obtained for clutter suppression under the guidance of polarization classification results as a priori knowledge. Finally, the experimental results demonstrate that: 1) the suboptimal JPolSAP processor with PMF steering vector can effectively enhance GMTI performance about 13 dB (or even up to 5 dB) relative to the worst (or best) single-polarization (S-pol) processor case and has the best robustness compared with the one with full-one steering vector; 2) polarization enhancement using PMF also obtains a good output gain of polarization filter, especially for quad-pol enhance-ent, which gains up to 2–3 dB with respect to the best output of S-pol processor, and the NCID technique can obtain good performance of moving-target detection; and 3) under the guidance of polarization classification results, the capability of clutter suppression can improve even up to 15 dB with respect to the one without classification.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Intrapulse Polyphase Coding System for Second Trip Suppression in a
           Weather Radar
    • Authors: Mohit Kumar;V. Chandrasekar;
      Pages: 3841 - 3853
      Abstract: This article describes the design and implementation of intrapulse polyphase codes for a weather radar system. Algorithms to generate codes with good correlation properties are discussed. Thereafter, a new design framework is described, which optimizes the polyphase code and corresponding mismatched filter, using a cost/error function, especially for weather radars. It establishes the performance of these intrapulse techniques with specific application toward second trip removal. The developed code is implemented on NASA D3R, which is a dual-frequency, dual-polarization, Doppler weather radar system.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A Large Scale Characterization of the Dielectric Properties of
           Heterogeneous Layered Rock Salt
    • Authors: Alina Badescu;
      Pages: 3854 - 3863
      Abstract: Although there are several methods described in the literature to accurately measure the complex permittivity of solid dielectrics at radio frequencies in a laboratory environment, none of these methods allow for a large-scale accurate characterization of natural ionic dielectrics. The work presented here reports results of the dielectric permittivity retrieved from in situ measurements in a Romanian salt mine. Measurements were performed in the 164–174-MHz bandwidth, over a propagation distance of 100 m. The characteristics of the layers of sedimentary salt are determined from measurements using a least mean square fitting algorithm, based on a detailed propagation model for a heterogeneous medium. The coupling of the instrumentation is also considered. The proposed approach demonstrates great promise for a large number of applications.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A Distribution and Structure Match Generative Adversarial Network for SAR
           Image Classification
    • Authors: Zhongle Ren;Biao Hou;Qian Wu;Zaidao Wen;Licheng Jiao;
      Pages: 3864 - 3880
      Abstract: Synthetic aperture radar (SAR) image classification is a fundamental research in the interpretation of SAR images. The previous methods are unilaterally based on statistical features or spatial features, which cannot capture features with complete SAR image characteristics and unavoidably limits the performance for classification. In this article, novel sample weighting and class adversarial training strategies are proposed to fuse complementary SAR characteristics. Based on these, a distribution and structure match auxiliary classifier generative adversarial network (DSM-ACGAN) is constructed for high-quality discriminative feature learning. Particularly, the characteristics of statistical distribution and spatial structure are jointly considered in class adversarial training of DSM-ACGAN. On the one hand, DSM-ACGAN sets the true SAR image characteristics as goals for the generator to learn generative models of each category. On the other hand, and more importantly, it guides the discriminator to simultaneously capture the desired statistical and structural features. Through the class adversarial processing, the discriminative feature learning progressively improves and contributes to classification. Additionally, class-balanced and plausible samples can be generated. Experimental results on three broad SAR images from different satellites confirm the effectiveness of class adversarial training and the superiority of discriminative feature learning in DSM-ACGAN. Visual performance and quantitative metrics also show the state-of-the-art performance of the novel model.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Signal Reconstruction Algorithm for Azimuth Multichannel SAR System Based
           on a Multiobjective Optimization Model
    • Authors: Yongwei Zhang;Wei Wang;Yunkai Deng;Robert Wang;
      Pages: 3881 - 3893
      Abstract: This article establishes a multiobjective optimization model to suppress the azimuth ambiguity power and noise simultaneously in signal reconstruction for a multichannel synthetic aperture radar (SAR) system. This multiobjective optimization model extends the theory of multichannel signal processing for reconstructing the SAR signal from the aliased signals. Linear scalarization and a quadratically constrained method for the multiobjective optimization model are applied to obtain $l_{1}$ norm optimization, $l_{2}$ norm optimization, and quadratically constrained optimization, respectively, in signal reconstruction. Azimuth ghosts can intuitively reflect the effects of azimuth ambiguity on SAR images. The $l_{1}$ norm optimization solution leads to a minimum upper bound of azimuth ghosts. A lowest azimuth ambiguity-to-signal ratio (AASR) can be derived by $l_{2}$ norm optimization. By relaxing the constraint of total ambiguity power suppression, one can obtain a minimum noise level in the case of quadratically constrained optimization. The reconstruction performances of the multiobjective optimization model in terms of AASR, signal-to-noise ratio (SNR), and signal-to-ambiguity-plus-noise ratio (SANR) are investigated with respect to the pulse repetition frequency (PRF) and compared with other methods for a multichannel SAR system.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Improved SMAP Dual-Channel Algorithm for the Retrieval of Soil Moisture
    • Authors: Mario Julian Chaubell;Simon H. Yueh;R. Scott Dunbar;Andreas Colliander;Fan Chen;Steven K. Chan;Dara Entekhabi;Rajat Bindlish;Peggy E. O’Neill;Jun Asanuma;Aaron A. Berg;David D. Bosch;Todd Caldwell;Michael H. Cosh;Chandra Holifield Collins;Jose Martínez-Fernández;Mark Seyfried;Patrick J. Starks;Zhongbo Su;Marc Thibeault;Jeffrey Walker;
      Pages: 3894 - 3905
      Abstract: The soil moisture active passive (SMAP) mission was designed to acquire L-band radiometer measurements for the estimation of soil moisture (SM) with an average ubRMSD of not more than 0.04 $text{m}^{3}/text{m}^{3}$ volumetric accuracy in the top 5 cm for vegetation with a water content of less than 5 kg/ $text{m}^{2}$ . Single-channel algorithm (SCA) and dual-channel algorithm (DCA) are implemented for the processing of SMAP radiometer data. The SCA using the vertically polarized brightness temperature (SCA-V) has been providing satisfactory SM retrievals. However, the DCA using prelaunch design and algorithm parameters for vertical and horizontal polarization data has a marginal performance. In this article, we show that with the updates of the roughness parameter $h$ and the polarization mixing parameters $Q$ , a modified DCA (MDCA) can achieve improved accuracy over DCA; it also allows for the retrieval of vegetation optical depth (VOD or $tau$ ). The retrieval performance of MDCA is assessed and compared with SCA-V and DCA using four years (April 1, 2015 to March 31, 2019) of in situ data from core validation sites (CVSs) and sparse networks. The assessment shows that SCA-V still outperforms all the implemented algorithms.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Fast and Latent Low-Rank Subspace Clustering for Hyperspectral Band
           Selection
    • Authors: Weiwei Sun;Jiangtao Peng;Gang Yang;Qian Du;
      Pages: 3906 - 3915
      Abstract: This article presents a fast and latent low-rank subspace clustering (FLLRSC) method to select hyperspectral bands. The FLLRSC assumes that all the bands are sampled from a union of latent low-rank independent subspaces and formulates the self-representation property of all bands into a latent low-rank representation (LLRR) model. The assumption ensures sufficient sampling bands in representing low-rank subspaces of all bands and improves robustness to noise. The FLLRSC first implements the Hadamard random projections to reduce spatial dimensionality and lower the computational cost. It then adopts the inexact augmented Lagrange multiplier algorithm to optimize the LLRR program and estimates sparse coefficients of all the projected bands. After that, it employs a correntropy metric to measure the similarity between pairwise bands and constructs an affinity matrix based on sparse representation. The correntropy metric could better describe the nonlinear characteristics of hyperspectral bands and enhance the block-diagonal structure of the similarity matrix for correctly clustering all subspaces. The FLLRSC conducts spectral clustering on the connected graph denoted by the affinity matrix. The bands that are closest to their separate cluster centroids form the final band subset. Experimental results on three widely used hyperspectral data sets show that the FLLRSC performs better than the classical low-rank representation methods with higher classification accuracy at a low computational cost.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Radio Frequency Tomography for Nondestructive Testing of Pillars
    • Authors: Tadahiro Negishi;Gianluca Gennarelli;Francesco Soldovieri;Yangqing Liu;Danilo Erricolo;
      Pages: 3916 - 3926
      Abstract: Pillars represent some of the commonest supporting elements of modern and historical buildings. Nondestructive testing methods can be applied to gain information about the status of these structural elements. Among them, ground penetrating radar (GPR) is a popular diagnostic tool for the assessment of concrete structures. Despite several theoretical and experimental studies on concrete structural evaluation by GPR have been reported, little work has been done so far with respect to pillars. Owing to their circular geometry, pillars are complex multiscattering environments, which render the interpretation of the radar images very challenging. This article deals with the application of radio frequency tomography as a nondestructive technique for imaging the inner structure of pillars. The main goal of the study is the assessment of the imaging performance that can be obtained in comparison to conventional GPR exploiting a multimonostatic configuration. Accordingly, potentialities and performance of multimonostatic and multiview/multistatic measurement configurations are herein investigated in the inverse scattering framework. For each measurement configuration, the regularized reconstruction of a point-like target and the spectral content are evaluated. The data inversion is carried out by means of the truncated singular value decomposition scheme. Tomographic reconstructions based on full-wave synthetic data are shown to support the comparative analysis.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Discrimination of Parallel and Perpendicular Insects Based on Relative
           Phase of Scattering Matrix Eigenvalues
    • Authors: Cheng Hu;Weidong Li;Rui Wang;Teng Long;V. Alistair Drake;
      Pages: 3927 - 3940
      Abstract: Current vertical-beam entomological radars record the polarization direction corresponding to the maximal ventral-aspect radar cross section (RCS) as the insect’s orientation. For so-called “parallel” insects, this direction is indeed their orientation; but for “perpendicular” insects, it is at right angles to the orientation. Current entomological radars cannot discriminate the parallel and perpendicular cases. This article shows here that discrimination is possible using the relative phase of the scattering matrix (SM) eigenvalues. Multifrequency fully polarimetric ventral aspect SM measurements of 80 insect specimens of 12 species have been made in a microwave anechoic chamber. The relationship of the polarization direction corresponding to the maximal RCS and the radar frequency has been analyzed, and from these results a method of discriminating parallel and perpendicular insects, based on the relative phase of the SM eigenvalues, is proposed. The method is applicable to X- and Ku-band observations, with a high correct-identification rate, and can be used with both fully polarimetric entomological radars and coherent rotating-polarization units, but not with the noncoherent rotating-polarization configuration used in traditional vertical-looking radars (VLRs). Finally, the performance of the method is discussed, and it is found that it has better performance for middle and large insects at X-band and small and middle insects at Ku-band.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Impacts of Ionospheric Irregularities on L-Band Geosynchronous Synthetic
           Aperture Radar
    • Authors: Yifei Ji;Yongsheng Zhang;Zhen Dong;Qilei Zhang;Dexin Li;Baidong Yao;
      Pages: 3941 - 3954
      Abstract: An L-band geosynchronous synthetic aperture radar (GEO SAR) system has to be confronted by an intractable issue of the decorrelations imposed by ionospheric irregularities. On the one hand, the phase and amplitude scintillations will bring about the decorrelation within the synthetic aperture and result in azimuth-imaging degradation. On the other hand, the imposed scintillation history is spatially decorrelated across the ultra-large GEO SAR scene. In this article, a signal model of the GEO SAR acquisitionis established with the two-way ionospheric transfer function (ITF) modulation to incorporate these two types of decorrelations. This model meanwhile takes the anisotropic and flowing irregularities into account. By using this model, the L-band GEO SAR azimuth-imaging is evaluated in terms of five indexes, whose performances are dependent on nine ionospheric parameters. Furthermore, the spatial correlation of the phase and intensity scintillation histories is investigated for the L-band GEO SAR scene, both in simulation and statistics. The statistical result implies a sized scene, in which the phase scintillation history tends to be consistent. Finally, the interferometric performance is investigated between the pure and contaminated GEO SAR images. The simulation result shows that the degradation of the interferometric coherence results from the in-aperture decorrelation.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Statistical Derivation of Wind Speeds From CYGNSS Data
    • Authors: Maria Paola Clarizia;Christopher S. Ruf;
      Pages: 3955 - 3964
      Abstract: In this article, a statistical methodology to estimate wind speed from CYGNSS observables is proposed and implemented. The approach uses the cumulative distribution function (cdf) of the observable and of the ground-truth reference winds. It depends only on the statistical distributions of the CYGNSS data and the wind speed, and therefore, is simpler to implement than alternative approaches requiring coincident matchups between the data and the ground truth. This cdf matching method produces retrieved winds with a probability density function that is very close to that of the ground-truth winds. When compared to the current CYGNSS baseline winds for fully developed seas, the cdf matching winds show better behavior and agreement with reference wind speeds over the low to medium wind speed range, which constitutes the majority of the wind population that drives the statistics used by the algorithm. The performance is robust with respect to measurement geometry and transmitter and receiver hardware parameters, with the exception of a dependence of the error on the GPS satellite identifier (ID), probably due to uncorrected variations in GPS equivalent isotropically radiated power (EIRP). Validation using modeled winds and winds measured by other satellites reveals that CYGNSS winds behave in a very similar manner as the winds modeled by the Global Data Assimilation System (GDAS).
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • FY-3D HIRAS Radiometric Calibration and Accuracy Assessment
    • Authors: Chunqiang Wu;Chengli Qi;Xiuqing Hu;Mingjian Gu;Tianhang Yang;Hanlie Xu;Lu Lee;Zhongdong Yang;Peng Zhang;
      Pages: 3965 - 3976
      Abstract: The High-Spectral Infrared Atmospheric Sounder (HIRAS) is a Fourier transform spectrometer onboard the fourth polar-orbiting FengYun 3D satellite (FY-3D). The FY-3D HIRAS provides interferogram measurements of Earth view radiance spectra in three infrared spectral bands at 29 cross-track positions, each with a $2 times 2$ array of field of views (FOVs). The HIRAS level 1 radiance data cover the spectral bands from 650 to 1135 cm−1 [long-wave (LW) band], 1210 to 1750 cm−1 [mid-wave (MW) band], and 2155 to 2550 cm−1 [short-wave (SW) band] with a spectral resolution of 0.625 cm−1. The radiometric calibration algorithm and the methods of refining the nonlinearity (NL) and the polarization correction coefficients on orbit are summarized in this article. The NL correction coefficients are derived by minimizing the spread of the responsivity functions derived from the measurements of the internal calibration target with varying temperatures. The polarization correction coefficients are derived from the cold space observations and the routine Earth scene measurements. The radiometric accuracy is assessed by comparing the HIRAS measurements to the collocated Cross-track Infrared Sounder (CrIS) observations and radiance simulations. The results show that, compared to CrIS, the radiometric differences are about 0.3 and 0.7 K for the LW and MW bands, respectively, and 0.5 K for the CO absorption and window regions in the SW band. The consistency of the radiometric calibration among the four FOVs is estimated to be within 0.2 K for most of the spectral domain. Some remaining issues for the FY-3D HIRAS are also discussed.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Distributed Scatterer Interferometry With the Refinement of Spatiotemporal
           Coherence
    • Authors: Mi Jiang;Andrea Monti Guarnieri;
      Pages: 3977 - 3987
      Abstract: The state-of-the-art techniques have demonstrated that coherence error degrades the performance of synthetic aperture radar (SAR) interferometry (InSAR) for distributed scatterers (DSs). This article aims at fully evaluating the influence of coherence error on DS InSAR time-series analysis. In particular, we present a methodology to increase the estimation accuracy of DS interferometry, with emphasis on spatiotemporal coherence refinement. The motive behind this is that bias removal and variance mitigation of sample coherence matrix impose optimum weighting for estimating phase series and geophysical parameters of interest, whereas maximization of temporal coherence in a reference network can avoid spatial error propagation during the least-squares adjustment. Rather than developing independent processing chains, we integrate this method into SqueeSAR technique and simultaneously take the advantage of StaMPS into consideration. Using simulation and real data over southwestern China, comprehensive comparisons before and after spatiotemporal coherence refinement are performed over various coherence scenarios. The results tested from different phase and displacement rate estimators validate the effectiveness of the presented method.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • $L$+ -Band+Ocean+Surface+Roughness&rft.title=IEEE+Transactions+on+Geoscience+and+Remote+Sensing&rft.issn=0196-2892&rft.date=2020&rft.volume=58&rft.spage=3988&rft.epage=3999&rft.aulast=Ainsworth;&rft.aufirst=Paul&rft.au=Paul+A.+Hwang;Thomas+L.+Ainsworth;"> $L$ -Band Ocean Surface Roughness
    • Authors: Paul A. Hwang;Thomas L. Ainsworth;
      Pages: 3988 - 3999
      Abstract: Surface wave spectral properties of centimeter to decameter (cmDm) wavelengths are of great interest to microwave remote sensing of the ocean. They are obviously different from the high-frequency extension of the wind-wave spectrum models developed for ocean science and engineering applications, which focus on the longer waves in the energetic peak region of the wave spectrum. For more than six decades, the cmDm waves are generally considered to be in the equilibrium range, and its spectral function has a constant slope: −5 or −4 in the 1-D frequency spectrum, and −3 or −2.5 in the 1-D wavenumber spectrum. The observed wind-wave spectral slopes, however, are not constant. As a result, the cmDm wave properties are significantly different from those inferred from an equilibrium spectrum model. Surface slope measurements are more suited for studying the cmDm waves. Microwave radar backscattering cross sections have been used to study the shorter range of cmDm waves. L-band lowpass-filtered mean square slope (LPMSS) is contributed by waves longer than about 0.6 m, here referred to as the decimeter to decameter (dmDm) waves. The analysis of LPMSS has improved the modeling of dmDm waves. Ultimately, the spectral slope variation is a critical characteristic of cmDm waves. The wave spectrum model formulated with the variable spectral slope consideration produces very good agreement with L-band scatterometer and reflectometer measurements.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Assessment of INSAT-3D Retrieved Temperature and Water Vapour With
           Collocated Radiosonde Measurements Over Indian Region
    • Authors: A. Hemanth Kumar;S. V. Sunilkumar;
      Pages: 4000 - 4005
      Abstract: This article evaluates the performance of Indian National Satellite System (INSAT)-3D sounder temperature (T) and water vapor (WV) by comparing with the collocated radiosonde observations obtained at different stations over Indian region. The assessment is carried out in terms of bias in the INSAT-3D measurements by assuming the radiosonde observations as a standard measurement technique. The study is carried out to find the performance of INSAT-3D sounder data on different surface types (land, coast, and ocean), seasons, and latitude zones. The main findings are the large bias in INSAT-3D-derived T and WV profiles over land compared to the coastal and oceanic stations, which may be attributed to the large contrasting surface emissivities over land. The WV measurements of INSAT-3D showed a relative dry bias of 15%–25% in the lower troposphere. Near the surface, a warm bias (~2 K) is observed over the land, and a cold bias (~1 K) is observed over the coastal and oceanic stations. Cold biases of ~1 K (land) and ~0.5 K (coastal and oceanic) are observed in the middle troposphere. The variability of the T bias is relatively large over land, particularly during winter and premonsoon. The variability of T bias is large in the 25°–35°N latitudinal zone mainly in winter followed by premonsoon, postmonsoon, and monsoon. A consistent warm bias of ~2–3 K is observed in the upper troposphere particularly at around 100 hPa on all the surface types in all seasons over all latitude zones. Thus, bias correction is advised in using these data for upper tropospheric and lower stratospheric (UTLS) studies.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • RFI Source Localization in Microwave Interferometric Radiometry: A Sparse
           Signal Reconstruction Perspective
    • Authors: Dong Zhu;Jun Li;Gang Li;
      Pages: 4006 - 4017
      Abstract: The Microwave Interferometric Radiometer with Aperture Synthesis (MIRAS) is the payload of the Soil Moisture and Ocean Salinity (SMOS) satellite mission led by the European Space Agency. Although the MIRAS operates at the protected L-band, it is perturbed by radio frequency interferences (RFIs) that contaminate the acquired remote sensing data and further deteriorate the total performance of SMOS mission. Accurate location information of these sources is crucial for switching off illegal RFI emitters or mitigating RFI impacts from contaminated data. This article addresses the localization of SMOS RFI sources from a perspective of sparse signal reconstruction (SSR), which exploits the sparsity of RFI sources in the spatial domain. Such an SSR strategy possesses superior (at least comparable) performances over existing RFI localization methods [e.g., discrete Fourier transformation (DFT) inversion and subspace-based direction-of-arrival (DOA) estimation] using only SMOS measurements and even under situations in the presence of data missing due to correlator failures.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A Novel Multitemporal Image-Fusion Algorithm: Method and Application to
           GOCI and Himawari Images for Inland Water Remote Sensing
    • Authors: Yulong Guo;Changchun Huang;Yali Zhang;Yuan Li;Weiqiang Chen;
      Pages: 4018 - 4032
      Abstract: A spectral–temporal-unmixing-based multitemporal image fusion (MTIF) algorithm is proposed to fuse Geostationary Ocean Color Imager (GOCI) and Himawari images. The algorithm was applied to two data sets. The fusions are quantitatively and qualitatively compared with four widely used algorithms. The results show that the MTIF algorithm performs better using both evaluation indexes and visual comparisons. For optical complex water monitoring, the MTIF-derived chlorophyll-a concentration ( ${C} _{{text {chla}}}$ ) map has better spatial detail and temporal trends compared with the other algorithms. For cloudy images, the MTIF algorithm can estimate part of the undercloud water reflectance information: when there are more than 32.6 cloud-free pixels in the current study area, the MTIF algorithm can successfully recover the under cloud information. The MTIF algorithm has great potential to advance the monitoring of optical complex inland water.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A Subspace Selection-Based Discriminative Forest Method for Hyperspectral
           Anomaly Detection
    • Authors: Shizhen Chang;Bo Du;Liangpei Zhang;
      Pages: 4033 - 4046
      Abstract: In this article, a new subspace selection-based discriminative forest (SSDF) method is proposed for the anomaly detection of hyperspectral remote sensing imagery. Most of the existing anomaly detection approaches construct a profile of background instances and then identify instances that do not conform to the background materials as anomalies. However, this type of method generally fails to avoid the background contamination caused by abnormal targets. In this case, we borrow from the concept of isolation and propose an isolation-based discriminative forest model which exploits subsampling rather than modeling the background instances. Furthermore, considering that the data volume of a hyperspectral image is usually huge, the proposed discriminative forest model explores a subspace selection process while splitting the leaf nodes of the binary trees to preserve those bands containing crucial abnormal target information and improve the reliability of the tree-splitting criterion. The proposed detector successfully integrates dimensionality reduction and the data-splitting technique to define pixels as anomaly or background. The extensive experimental results obtained with four hyperspectral data sets demonstrate that the proposed SSDF algorithm outperforms the other state-of-the-art algorithms and hence provides a new perspective for the anomaly detection of hyperspectral remote sensing imagery.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Deep Learning for Multilabel Remote Sensing Image Annotation With
           Dual-Level Semantic Concepts
    • Authors: Panpan Zhu;Yumin Tan;Liqiang Zhang;Yuebin Wang;Jie Mei;Hao Liu;Mengfan Wu;
      Pages: 4047 - 4060
      Abstract: Multilabel remote sensing (RS) image annotation is a challenging and time-consuming task that requires a considerable amount of expert knowledge. Most existing RS image annotation methods are based on handcrafted features and require multistage processes that are not sufficiently efficient and effective. An RS image can be assigned with a single label at the scene level to depict the overall understanding of the scene and with multiple labels at the object level to represent the major components. The multiple labels can be used as supervised information for annotation, whereas the single label can be used as additional information to exploit the scene-level similarity relationships. By exploiting the dual-level semantic concepts, we propose an end-to-end deep learning framework for object-level multilabel annotation of RS images. The proposed framework consists of a shared convolutional neural network for discriminative feature learning, a classification branch for multilabel annotation and an embedding branch for preserving the scene-level similarity relationships. In the classification branch, an attention mechanism is introduced to generate attention-aware features, and skip-layer connections are incorporated to combine information from multiple layers. The philosophy of the embedding branch is that images with the same scene-level semantic concepts should have similar visual representations. The proposed method adopts the binary cross-entropy loss for classification and the triplet loss for image embedding learning. The evaluations on three multilabel RS image data sets demonstrate the effectiveness and superiority of the proposed method in comparison with the state-of-the-art methods.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Air-Ground Impedance Matching by Depositing Metasurfaces for Enhanced GPR
           Detection
    • Authors: Tong Hao;Wuan Zheng;Wenchao He;Kaiqiang Lin;
      Pages: 4061 - 4075
      Abstract: Deeply buried inclusions such as pipes and cables cannot be detected when the air–ground interface suffers severe impedance mismatch, resulting in little electromagnetic (EM) signals penetrating the subsurface, even before the scattering and reflection from the buried inclusions occur. Therefore, increasing the penetration depth by effectively enhancing the EM transmission into the lossy subsurface domain is of great importance. In this article, we present our simulation and experimental results of a type of antireflection metasurfaces that can sufficiently enhance the transmission from the air to the subsurface for ground-penetrating radar (GPR) applications. The proposed metasurface design consists of an array of closed ring resonators (CRRs) and metallic mesh on each side of a dielectric spacer, showing near-perfect antireflection. The corresponding enhanced transmission is only limited by the material losses of the metasurface itself. Through the geometry optimizations, three metasurface designs have been numerically and experimentally demonstrated for the dry, medium moist, and wet scenarios. It is discovered that the transmission into the wet foam brick can be increased by up to 50% when the metasurface is in place. The metasurface-based transmission enhancement is also relatively insensitive to the deviation of the permittivity of the material under test (MUT). Our real-world GPR experiments demonstrate that an undetectable buried pipe can be distinguished if the metasurface is placed at the air–ground interface. The proposed metasurface approach provides a promising solution to the impedance matching problems for nondestructive testing applications.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A 2-D Pseudospectral Time-Domain (PSTD) Simulator for Large-Scale
           Electromagnetic Scattering and Radar Sounding Applications
    • Authors: Yang Lei;Mark S. Haynes;Darmindra Arumugam;Charles Elachi;
      Pages: 4076 - 4098
      Abstract: This article discusses the implementation of a 2-D pseudospectral time-domain (PSTD) full-wave simulator for solving large-scale low-frequency (e.g., HF) electromagnetic (EM) scattering problems with the application of radar sounding of planetary subsurfaces. Compared to other computational EM algorithms, the PSTD solver is both memory-efficient and accurate for sounding applications. New domain designs are developed to efficiently simulate 2-D scattering of half-space media for normal and oblique incidence from arbitrary wave sources. As a validation of the PSTD simulator, the simulated 2-D scattering radar cross width (RCW) is compared with the analytical solutions of both point targets (dielectric cylinders) and distributed targets (random rough surfaces), for the first time, where the frequency and angular (bistatic scattering) dependence are studied with various choices of grid sampling resolution. Furthermore, the PSTD solver is applied to passive synthetic aperture radar (SAR) sounding problems (single transmitter and several receivers), for the first time, where various scenarios (e.g., cylinder, surface, and volume) are demonstrated and the targets are correctly resolved after focusing, indicating an accurate simulation of the phase history. Finally, an example of using the solver is shown for emulating 3-D large-scale radar sounding problems with cross-track surface and subsurface scattering. This is particularly useful to simulate radar sounding returns and SAR-focused imagery of large-scale subsurface structures to better support planetary missions with radar sounding instruments.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Remote-Sensing Image Superresolution Based on Visual Saliency Analysis and
           Unequal Reconstruction Networks
    • Authors: Libao Zhang;Donghui Chen;Jie Ma;Jue Zhang;
      Pages: 4099 - 4115
      Abstract: Remote-sensing images (RSIs) generally have strong spatial characteristics for surface features. Various ground objects, such as residential areas, roads, forests, and rivers, differ substantially. According to this visual attention characteristic, regions with complicated texture features require more realistic details to reflect a better description of the topography, while regions such as farmlands should be smooth and have less noise. However, most existing single-image superresolution (SISR) methods fail to fully utilize these properties and therefore apply a uniform reconstruction strategy to the whole image. In this article, we propose a novel saliency-driven unequal single-image reconstruction network in which the demands of various regions in the superresolution (SR) process are distinguished by saliency maps. First, we design a new gradient-based saliency analysis method to produce more accurate saliency maps with imagewise annotations. The method utilizes the superiority of a multireception field to extract both high-level features and low-level features. Second, we propose a novel saliency-driven gate conditional generative adversarial network, where the saliency map is regarded as a medium during the training procedure of the whole network. The saliency map is regarded as a pixelwise condition in a generator to enhance the training capability of the network. Additionally, we design a new loss function that combines normalized content loss, saliency-driven perceptual loss, and gate-control adversarial loss to further refine details of texture-complex areas for RSIs. We evaluate the performance of our algorithm and compare it with many other state-of-the-art SR methods using a remote-sensing data set. The experimental results show that our approach achieves the optimal outcome in salient areas. Our method attains the best effect on global quality and visual performance.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Hyperspectral Classification With Noisy Label Detection via
           Superpixel-to-Pixel Weighting Distance
    • Authors: Bing Tu;Chengle Zhou;Danbing He;Siyuan Huang;Antonio Plaza;
      Pages: 4116 - 4131
      Abstract: Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral–spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use $K$ nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Subpixel SAR Image Registration Through Parabolic Interpolation of the 2-D
           Cross Correlation
    • Authors: Luca Pallotta;Gaetano Giunta;Carmine Clemente;
      Pages: 4132 - 4144
      Abstract: In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Transceive Beamforming With Accurate Nulling in FDA-MIMO Radar for Imaging
    • Authors: Lan Lan;Guisheng Liao;Jingwei Xu;Yuhong Zhang;Bin Liao;
      Pages: 4145 - 4159
      Abstract: Beamforming plays a crucial role in synthetic aperture radar (SAR) for interference mitigation and ambiguity unaliasing. In this article, a series of novel beamforming methods for SAR systems is proposed based on nulling the transceive beampattern accurately in frequency diverse array (FDA)-multiple-input and multiple-output (MIMO) scheme. In general, these methods are implemented by assigning artificial interferences with prescribed powers within the given rectangular regions in the joint transmit–receive spatial frequency domain. In specific, according to the predefined null depths, closed-form expressions of artificial interference powers are first formulated. Then, iteration algorithms are developed to update the interference-plus-noise covariance matrix and the designed weight vector. In such a way, a trough-like transceive beampattern with arbitrarily distributed broadened nulls is formed in the joint transmit–receive spatial frequency domain. As a result, interferences mixed in signals received by SAR can be suppressed effectively. Numerical simulations and experimental results are provided to corroborate the effectiveness of the proposed methods.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Focusing of Bistatic SAR With Curved Trajectory Based on Extended Azimuth
           Nonlinear Chirp Scaling
    • Authors: Zhigui Wang;Mei Liu;Gengting Ai;Pengfei Wang;Kunfeng Lv;
      Pages: 4160 - 4179
      Abstract: The focusing of bistatic synthetic aperture radar (BiSAR) data is more challenging than the traditional monostatic counterparts because of the strong range–azimuth coupling of echo signal, and the range cell migration (RCM) and Doppler frequency modulation (FM) rate, which are caused by complex geometric configuration. Although several monostatic algorithms have been modified to handle general bistatic cases, these algorithms are derived from the assumption that the flying platforms are moving along a linear trajectory with uniform velocity. In practical situation, the flight path of the spaceborne SAR platform inevitably deviates from the ideal trajectory in the long integration time. In this case, besides the influence of the inherent geometric configuration of BiSAR, the curved trajectory of the platforms also causes an additional range–azimuth coupling and the spatial variance of RCM and Doppler FM rate, which cannot be processed by the traditional algorithms. In this article, considering the curved orbit of the SAR platforms and the motion of ground targets caused by the Earth’s rotation, a high-order motion range model is proposed. Based on the range model, the spatial variance characteristic of the BiSAR with curved trajectory is analyzed. Then, an extended azimuth nonlinear chirp scaling (EANLCS) algorithm with an addition of highly varying residual Doppler centroid correction for BiSAR with curved trajectory is proposed. Simulation results show the effectiveness of the range model and the modified algorithm.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Superpixel Contracted Graph-Based Learning for Hyperspectral Image
           Classification
    • Authors: Philip Sellars;Angelica I. Aviles-Rivero;Carola-Bibiane Schönlieb;
      Pages: 4180 - 4193
      Abstract: A central problem in hyperspectral image (HSI) classification is obtaining high classification accuracy when using a limited amount of labeled data. In this article we present a novel graph-based semi-supervised framework to tackle this problem. Our framework uses a superpixel approach, allowing it to define meaningful local regions in HSIs, which with high probability share the same classification label. We then extract spectral and spatial features from these regions and use them to produce a contracted weighted graph-representation, where each node represents a region rather than a pixel. The graph is then fed into a graph-based semi-supervised classifier which gives the final classification. We show that using superpixels in a graph representation is an effective tool for speeding up graphical classifiers applied to HSIs. We demonstrate through exhaustive quantitative and qualitative results that our proposed method produces accurate classifications when an incredibly small amount of labeled data is used. We show that our approach mitigates the major drawbacks of existing approaches, resulting in our approach outperforming several comparative state-of-the-art techniques.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill
           Observations
    • Authors: Martine M. Espeseth;Camilla Brekke;Cathleen E. Jones;Benjamin Holt;Anthony Freeman;
      Pages: 4194 - 4214
      Abstract: The effects of both system additive and multiplicative noise on the X-, C-, and L-band synthetic aperture radar (SAR) data covering oil slicks are examined. Prior studies have attempted to characterize such oil slicks, primarily through analysis of polarimetric SAR data. In this article, we factor in system noise that is added to the backscattered signal, introducing artifacts that can easily be confused with random and volume scattering. This confusion occurs when additive and/or multiplicative system noise dominates the measured backscattered signal. Polarimetric features used in this article are shown to be affected by both additive and multiplicative system noise, some more than others. This article highlights the importance of considering specifically multiplicative noise in the estimation of the signal-to-noise ratio (SNR). The SNR based on additive noise should at least be above 10 dB and the SNR involving both additive and multiplicative noise should at least be above 0 dB. The SNR from TerraSAR-X (TS-X) and Radarsat-2 (RS-2) is below 0 dB for the majority of the oil slick pixels when considering both the additive and multiplicative noise, rendering these data unsuitable for any analysis of the scattering properties and characterization. These results are in contrast to the reduced impact of noise on oil slicks detected by the L-band UAVSAR system. In particular, we find that there is no need to invoke exotic scattering mechanisms to explain the characteristics of the data. We also recommend a noise subtraction for any polarimetric scattering analysis.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • On the Use of Lateral Wave for the Interlayer Debonding Detecting in an
           Asphalt Airport Pavement Using a Multistatic GPR System
    • Authors: Lilong Zou;Li Yi;Motoyuki Sato;
      Pages: 4215 - 4224
      Abstract: In this article, we focus on the detection of the interlayer debonding of the asphalt airport pavement by the ground-penetrating radar (GPR) system. Since the interlayer debonding usually occurs in the shallow region of the asphalt airport pavement (several centimeters), it is difficult to interpret the anomalies or the defects from the GPR signals composed of many waves under the boundary conditions. Moreover, the wavelength of the ordinary GPR system is over several centimeters. Therefore, the spatial resolution of the system is not accurate enough to consider the millimeter thickness of the debonding layer. To overcome these problems, we propose a new method based on evaluating the lateral wave behavior of common midpoint (CMP) gathers collected by a multiple static GPR system. The multistatic GPR system is a stepped frequency continuous wave (SFCW) radar system, which consists of eight transmitting and eight receiving bowtie antennas. The system operates in the frequency range from 50 MHz to 1.5 GHz. After the validation of the simulation, the results of the interlayer debonding detection were evaluated by a field experiment obtained at Tokyo International Airport. The proposed method can detect the debonding layers which are less than 1 mm. Also, it is shown that our proposed method has a high consistency with the conventional acoustic finding method in the field measurement. It provides an innovative and effective method for the interlayer debonding detection of a partially damaged airport asphalt pavement, which is difficult to be observed by the ordinary GPR signals.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Identification of Rain and Low-Backscatter Regions in X-Band Marine Radar
           Images: An Unsupervised Approach
    • Authors: Xinwei Chen;Weimin Huang;
      Pages: 4225 - 4236
      Abstract: In this article, an unsupervised clustering-based method for identifying rain-contaminated and low-backscatter regions in X-band marine radar images is presented. Rain blurs the wave signatures of radar images, and low-backscatter images caused by calibration errors or too-low wind speed contain little or no wave signatures. In both cases, ocean surface parameter measurement using X-band marine radar will be negatively affected. Four types of features can be extracted based on the distinct difference in texture and pixel intensity distribution between rain-free, rain-contaminated, and low-backscatter regions. Features extracted from each pixel are combined into a feature vector and mapped onto a $10times 10$ -neuron self-organizing map (SOM). Then, the hierarchical agglomerative clustering algorithm is introduced, which clustered those neurons into three types (i.e., rain-free, rain-contaminated, and low-backscatter). The method is validated using the shipborne marine radar data collected on the East Coast of Canada. The good agreement between the pixel-based clustering results and manually segmented reference images indicates that both rain-contaminated and low-backscatter regions can be identified effectively using the proposed method.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Spatial–Spectral Feature Extraction via Deep ConvLSTM Neural Networks
           for Hyperspectral Image Classification
    • Authors: Wen-Shuai Hu;Heng-Chao Li;Lei Pan;Wei Li;Ran Tao;Qian Du;
      Pages: 4237 - 4250
      Abstract: In recent years, deep learning has presented a great advance in the hyperspectral image (HSI) classification. Particularly, long short-term memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. However, the loss of spatial information makes it quite difficult to obtain better performance. In order to address this problem, two novel deep models are proposed to extract more discriminative spatial–spectral features by exploiting the convolutional LSTM (ConvLSTM). By taking the data patch in a local sliding window as the input of each memory cell band by band, the 2-D extended architecture of LSTM is considered for building the spatial–spectral ConvLSTM 2-D neural network (SSCL2DNN) to model long-range dependencies in the spectral domain. To better preserve the intrinsic structure information of the hyperspectral data, the spatial–spectral ConvLSTM 3-D neural network (SSCL3DNN) is proposed by extending LSTM to the 3-D version for further improving the classification performance. The experiments, conducted on three commonly used HSI data sets, demonstrate that the proposed deep models have certain competitive advantages and can provide better classification performance than the other state-of-the-art approaches.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A Closed-Form Robust Cluster-Analysis-Based Multibaseline InSAR Phase
           Unwrapping and Filtering Algorithm With Optimal Baseline Combination
           Analysis
    • Authors: Zhihui Yuan;Zhong Lu;Lifu Chen;Xuemin Xing;
      Pages: 4251 - 4262
      Abstract: Phase unwrapping (PU) and phase filtering are the key procedures for the interferometric synthetic aperture radar (InSAR) technology. As one of the most popular multibaseline PU (MBPU) algorithms, the cluster-analysis (CA)-based MBPU algorithm still has some problems that need to be improved. To begin with, the cluster ambiguity vector is obtained by searching the nearest integer point to the cluster centerline with known slope and intercept in the search space. It will be time-consuming and inconvenient when the number of baselines or the search space is too large. In addition, they do not have the capacity of phase filtering. Moreover, they do not consider the impact of different baseline combinations on the performance of the CA-based MBPU algorithm. For these reasons, a novel CA-based MBPU and filtering (MBPUF) algorithm is proposed in this article. The main contributions of this article are that it gives the closed-form solving formulas of the cluster ambiguity vector to improve the efficiency of the CA-based MBPU algorithm, proposes a novel MB InSAR phase-filtering strategy that makes the CA-based MBPU algorithm capable of solving the phase-discontinuity problem and improving the height-reconstruction accuracy simultaneously, and utilizes the optimal baseline combination to improve the robustness of the CA-based MBPU algorithm. Theoretical analysis and experiments on both simulated and real MB InSAR data sets show the effectiveness and robustness of the proposed closed-form robust CA-based MBPUF algorithm.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Multilabel Sample Augmentation-Based Hyperspectral Image Classification
    • Authors: Qiaobo Hao;Shutao Li;Xudong Kang;
      Pages: 4263 - 4278
      Abstract: The quantity and quality of training samples have a great influence on the performance of most hyperspectral image classification approaches. However, in a real scenario, manually annotating a large number of accurate training samples is extremely labor-intensive and time-consuming. In this article, a multilabel training sample augmentation method is proposed. Instead of giving an exact label to each pixel, we just precisely label a small number of pixels by giving them a single label (called single-label samples) and annotate a large number of pixels in certain regions together by giving them multiple labels (called multilabel samples). Furthermore, in order to make full use of the multilabel training samples, a superpixel segmentation and recursive filtering-based method is proposed. The proposed method consists of the following major steps: recursive filtering-based feature extraction, superpixel-based segmentation, and spectral–spatial similarity-based mislabeled sample removal. Experimental results demonstrate that the proposed method can significantly improve the classification accuracy of multiple classifiers by using the multilabel training samples.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • A MultiKernel Domain Adaptation Method for Unsupervised Transfer Learning
           on Cross-Source and Cross-Region Remote Sensing Data Classification
    • Authors: Wei Liu;Rongjun Qin;
      Pages: 4279 - 4289
      Abstract: Labeling remote sensing data for classification is labor-intensive and time-consuming. Transfer learning (TL), under such context, is attracting increasing attention as it aims to harness information from data set of other regions where labels are readily available. The central topic of concern is to homogenize the large disparities of feature distribution of different data set through domain adaptation (DA). This article proposes a novel DA method for unsupervised TL, namely, multikernel jointly domain matching (MKJDM), which by definition considers multiple kernels as opposed to the currently popular single-kernel methods for measuring the distances between distributions. The single-kernel methods minimize the distances of feature distribution between the source domain (data set with training labels) and the target domain (data set to be classified) through, for example, maximum mean discrepancy (MMD) metric, formed under a kernel function mapping, while the multikernel version (MK-MMD) uses different kernel functions to encapsulate multiple aspects of distribution discrepancies, and is, therefore, more capable of distance minimization. Our MKJDM implementation also considers simultaneously aligning marginal and class conditional distributions and reweight for each instance, which further improves the performance. Two experiments performed on remote sensing images and multi-modal data sets (i.e., Orthophoto and Digital Surface Models), with regions of different countries with distinctly different land patterns serving as source and target domain data, show that the overall accuracies are improved by 37.28% and 46.62% after applications of our MKJDM method. An additional comparative experiment with five state-of-the-art DA methods also demonstrates that our method achieves the best performance.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Assimilation of SAR Ice and Open Water Retrievals in Environment and
           Climate Change Canada Regional Ice-Ocean Prediction System
    • Authors: Alexander S. Komarov;Alain Caya;Mark Buehner;Lynn Pogson;
      Pages: 4290 - 4303
      Abstract: In this article, we evaluate the impact of assimilating spaceborne synthetic aperture radar (SAR) data in an Arctic regional ice analysis system over a year cycle. Ice and water information was automatically extracted from more than 7000 RADARSAT-2 HH-HV ScanSAR Wide images acquired over the Canadian Arctic and adjacent waters throughout the entire year 2013. A quality-control procedure was specifically developed and applied to reduce the number of erroneous SAR retrievals. To assess the impact of SAR ice and water retrievals on the Environment and Climate Change Canada (ECCC) Regional Ice-Ocean Prediction System (RIOPS) ice concentration analyses, we designed a set of data assimilation experiments with and without the inclusion of SAR retrievals. Our verification results suggest that the assimilation of SAR-derived retrievals considerably improves ice concentration analyses in the situations where high spatial resolution is important (e.g., near land and over small inland lakes). Furthermore, SAR retrievals are particularly useful over the areas where the Canadian Ice Service’s (CIS) manually derived ice products (such as Image Analyses, daily and weekly ice charts) are not available or have limited coverage. The three-satellite RADARSAT Constellation Mission (RCM) launched in June 2019 will significantly increase the temporal frequency of SAR data. According to the most recent CIS estimate, more than 54 000 RCM images a year will be acquired over the CIS areas of interest. Therefore, the assimilation of SAR retrievals from RCM should further enhance automated ice concentration analyses products.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Hyperspectral Image Super-Resolution by Band Attention Through Adversarial
           Learning
    • Authors: Jiaojiao Li;Ruxing Cui;Bo Li;Rui Song;Yunsong Li;Yuchao Dai;Qian Du;
      Pages: 4304 - 4318
      Abstract: Hyperspectral image (HSI) super-resolution (SR) is a challenging task due to the problems of texture blur and spectral distortion when the upscaling factor is large. To meet these two challenges, band attention through the adversarial learning method is proposed in this article. First, we put the SR process in a generative adversarial network (GAN) framework, so that the resulted high-resolution HSI can keep more texture details. Second, different from the other band-by-band SR method, the input of our method is of full bands. In order to explore the correlation of spectral bands and avoid the spectral distortion, a band attention mechanism is proposed in our generative network. A series of spatial–spectral constraints or loss functions is imposed to guide the training of our generative network so as to further alleviate spectral distortion and texture blur. The experiments on the Pavia and Cave data sets demonstrate that the proposed GAN-based SR method can yield very high-quality results, even under large upscaling factor (e.g., $8times $ ). More importantly, it can outperform the other state-of-the-art methods by a margin which demonstrates its superiority and effectiveness.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • On the Value of Available MODIS and Landsat8 OLI Image Pairs for MODIS
           Fractional Snow Cover Mapping Based on an Artificial Neural Network
    • Authors: Jinliang Hou;Chunlin Huang;Ying Zhang;Jifu Guo;
      Pages: 4319 - 4334
      Abstract: This article investigates how to select the optimal Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI image pairs for MODIS fractional snow cover (FSC) mapping using an artificial neural network (ANN). Four issues are discussed, including date selection, location selection, priority of date and location, and global and regional monitoring of MODIS FSC with ANNs. We propose using the histogram quadratic distance to define the similarity between the ANN training and the target test scene, which was used to quantify the representativeness of the training samples. We use the case study of MODIS FSC mapping of North Xinjiang, China, in the 2014–2015 snow season as an example. Thirty-eight experiments were designed. The experimental results demonstrate that the ANN-based FSC estimation accuracy outperformed the MODIS FSC product, with an average RMSE of 0.17, ${R}$ exceeds 0.8, and the total snow cover area was estimated more accurately in most cases. For a target test scene, we preliminarily inferred that the best method is to develop an ANN using image pairs of another location with the highest similarity in the same acquisition time, using historical image pairs of the target scene with the highest similarity is the second choice, and using historical image pairs from another location with a high similarity is the third choice. For global- and regional-scale MODIS FSC mapping with ANNs, we formulated the strategy of initially determining a reasonable location and subsequently selecting the acquisition date of the image pairs to guarantee that the training data set represents the entire study area well.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • High Spectral Infrared Atmospheric Sounder (HIRAS): System Overview and
           On-Orbit Performance Assessment
    • Authors: Chengli Qi;Chunqiang Wu;Xiuqing Hu;Hanlie Xu;Lu Lee;Fang Zhou;Mingjian Gu;Tianhang Yang;Chunyuan Shao;Zhongdong Yang;Peng Zhang;
      Pages: 4335 - 4352
      Abstract: The High Spectral Infrared Atmospheric Sounder (HIRAS) is the first Chinese Fourier Transform Michelson interferometer onboard the FengYun 3D (FY-3D) polar-orbiting meteorological satellite launched on November 15, 2017. The FY-3D HIRAS provides infrared (IR) radiance spectra measurements in three spectral bands: the long-wave IR (LWIR) band from 650 to 1135 cm−1, middle-wave IR (MWIR) band from 1210 to 1750 cm−1, and short-wave IR (SWIR) band from 2155 to 2550 cm−1. The ground system processes the interferogram measurements into calibrated radiance spectra. In each cross-track scan, there are 29 observations, each with a field-of-regard (FOR) comprising an array of $2times 2$ field of views. In a six-month intensive campaign period, the HIRAS system was tuned, characterized, and validated. For the operational Level 1 product, the radiance noise levels meet the specifications. The spectral frequency accuracy was improved by maximizing the spectral correlation between the measured and simulated spectra by tuning the instrument-line-shape parameters. The absolute spectral frequency biases are less than 3 part per million (ppm) for all the three bands, and spectral bias standard deviations are less than 3 ppm in the LWIR and MWIR bands, and are about 3–5 ppm in the SWIR band. The radiometric calibration uncertainties were assessed by the comparisons of the radiance spectra between HIRAS and other IR hyperspectral sensors on different satellites. The radiance differences of the cross-sensor comparisons are in general less than 0.3, 0.7, and 1.0 K in the LWIR, MWIR, and SWIR bands, respectively. The HIRAS spectra were also compared with the spectra simulated with a fast radiative transfer model. Some remaining issues for the FY-3D HIRAS are also discussed.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Hierarchical Semantic Propagation for Object Detection in Remote Sensing
           Imagery
    • Authors: Chunyan Xu;Chengzheng Li;Zhen Cui;Tong Zhang;Jian Yang;
      Pages: 4353 - 4364
      Abstract: Object detection in remote sensing imagery is a critical yet challenging task in the field of computer vision due to the bird’s-eye-view perspective. Although existing object detection approaches in remote sensing imagery have achieved great advances through the utilization of deep features or rotation proposals, but they give insufficient consideration to multilevel semantic information and its propagation for guiding the learning process. Accordingly, in this article, we propose a hierarchical semantic propagation (HSP) framework to boost object detection performance in remote sensing imagery, which is better able to propagate hierarchical semantic information among different components in a unified network. Given a remote sensing image as input, the HSP framework can detect instances of semantic objects belonging to certain categories in an end-to-end way. First, the multiscale representation is captured by a basic feature pyramid network, which can hierarchically combine spatial attention details and the global semantic structure in order to learn more discriminative visual features. Second, the soft-segmentation prediction is used as an auxiliary objective in the intermediate layer of our HSP; its output instance-aware semantic information can be propagated to suppress noisy background information and thereby guide the proposal generation in the region proposal network. By further propagating this hierarchical semantic information into the region of interest module, we can then predict the object category information and the corresponding horizontal and oriented bounding boxes. Comprehensive evaluations on three benchmark data sets demonstrate the superiority of our HSP to the existing state-of-the-art methods for object detection in remote sensing imagery.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Translational Motion Compensation for ISAR Images Through a Multicriteria
           Decision Using Surrogate-Based Optimization
    • Authors: Deniz Ustun;Abdurrahim Toktas;
      Pages: 4365 - 4374
      Abstract: Inverse synthetic aperture radar (ISAR) image is constructed using 2-D spatial distributions of the radar cross section of a target. ISAR data gathered from moving targets might include interphase error that causes a blurry effect in the ISAR image. In this article, an efficient motion compensation (MC) scheme depending on multicriteria decision using surrogate-based optimization (SbO) for minimizing the entropy and maximizing the sharpness of the images is proposed to remove the blur from the images. In order to provide a multicriteria decision, Pareto optimality is exploited to balance two criteria of the entropy and sharpness synchronously. A signal with an interphase error is input to the MC system for determining the global optimal motion parameters of the velocity and acceleration so as to focus on the ISAR image. The proposed scheme is implemented to four simulated ISAR scenarios reported elsewhere through two aircraft models for performance demonstration and comparison with artificial bee colony (ABC), differential evolution (DE), and particle swarm optimization (PSO) implemented in the literature. It is pointed out that the proposed scheme is more successful and efficient in view of the image focusing quality as well as the numerical results such as the motion parameters, the entropy and sharpness, and structural similarity (SSIM) index. The results also show that the SbO outperforms the other methods as the velocity and acceleration increase.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • An Indicator and Min-Cost Approach for Shoreline Extraction From Satellite
           Imagery in Muddy Coasts
    • Authors: Yang Zhang;
      Pages: 4375 - 4386
      Abstract: Shoreline location plays a key role in coastal research, management, and engineering. Remote sensing enables the quantification of shoreline information with the large spatial extent and high temporal frequency. Driven by river discharge and ocean dynamics, muddy coasts exhibit complicated spatiotemporal variations. It is essential, yet challenging to extract effective shoreline features from satellite images. Taking the Yellow River Delta coast in China as the study area, we present an indicator and min-cost approach to extract the shoreline in muddy coasts. The shoreline is represented as a set of linearly connected central points with high shoreline probabilities, and a set of image and spatial indicators are developed to assess these probabilities. The Salient Value indicator integrates the gradient magnitude and the edge intensity to detect the boundary strength; the Regional Difference indicator separates the water/land class from edge intensity to measure the possibility of being water or land; and the Seaward Distance indicator spatially distinguishes the true shoreline from other spectrally similar boundaries. A cost function combines these indicators to evaluate the local shoreline possibilities. A shoreline set is produced by an improved min-cost path method to evaluate the overall shoreline possibilities. The optimal shoreline paths are selected based on the parameter analyses of the shoreline set. The performance of the approach is confirmed by comparing with the ground truth and state-of-the-art methods. The effectiveness of the approach is tested for different spatial resolution data and coastal environments.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Validation of New Sea Surface Wind Products From Scatterometers Onboard
           the HY-2B and MetOp-C Satellites
    • Authors: Zhixiong Wang;Ad Stoffelen;Juhong Zou;Wenming Lin;Anton Verhoef;Yi Zhang;Yijun He;Mingsen Lin;
      Pages: 4387 - 4394
      Abstract: The new Ku-band scatterometer (HSCAT-B) onboard the HY-2B satellite was launched on October 25, 2018, and soon after the C-band scatterometer (Advanced Scatterometer (ASCAT)-C) onboard the MetOp-C satellite was launched on November 6, 2018. This article aims to validate the new sea surface wind products from them, and also to summarize the common issues in current scatterometer wind products. Thus, other scatterometer data are also used for comparisons, including the C-band MetOp-B/ASCAT, and Ku-band SCATSAT-1/OSCAT2 and HY-2A/SCAT winds. In this study, the C-band and Ku-band scatterometer wind products were each reproduced using the same procedures, in terms of backscatter calibration, wind retrieval, and quality control. The scatterometer winds are compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 winds or buoy winds, and the results show that the quality of ASCAT-C winds is almost the same as the well-known ASCAT-B; the HSCAT-B winds show quite good quality and similar validating statistics as ASCAT winds. Noticeable wind-speed-dependent biases are found in all Ku-band scatterometer winds, which suggests that refinements are needed for the NSCAT-4 geophysical model function, especially in terms of wind speed dependence for all incidence angles.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • ALERT: Adversarial Learning With Expert Regularization Using Tikhonov
           Operator for Missing Band Reconstruction
    • Authors: Litu Rout;
      Pages: 4395 - 4405
      Abstract: The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth’s surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Deep Learning for Regularly Missing Data Reconstruction
    • Authors: Xintao Chai;Genyang Tang;Shangxu Wang;Ronghua Peng;Wei Chen;Jingnan Li;
      Pages: 4406 - 4423
      Abstract: Inspired by image-to-image translation, we applied deep learning (DL) to regularly missing data reconstruction, aimed at translating incomplete data into their corresponding complete data. With this purpose in mind, we first construct a network architecture based on an end-to-end U-Net convolutional network, which is a generic DL solution for various tasks. We then meticulously prepare the training data with both synthetic and field seismic data. This article is implemented in Python based on Keras (a high-level DL library). We described the network architecture, the training data, and the training settings in detail. For training the network, we employed a mean-squared-error loss function and an Adam optimization algorithm. Next, we tested the trained network with several typical data sets, achieving good performances (even in the presence of big gaps) and validating the feasibility, effectiveness, and generalization capability of the assessed framework. The feature maps for a sample going through the well-trained network are uncovered. Compared with the f-x prediction interpolation method, DL performs better and is capable of avoiding several assumptions (e.g., linearity, sparsity, etc.) associated with conventional interpolation methods. We demonstrated the influences of the network depth, the kernel size of the convolution window, and the pooling function on the DL results. We applied the trained network to dense data reconstruction successfully. The proposed method can overcome noise to some extent. We finally discussed some practical aspects and extensions of the evaluated framework.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Spatial Variability of Electric Field Implied by Common Dielectric
           Effective Medium Models
    • Authors: Chen Guo;Priyanka Dutta;Gary Mavko;
      Pages: 4424 - 4435
      Abstract: Remote sensing measurements of Earth materials are always made at scales much larger than individual grains and cavities, yielding only upscaled effective properties. An “effective medium” is an idealized uniform material that has the same measured properties as the real mixture. A uniform electric field applied to the ideal effective medium remains uniform within the sample; however, the same electric field applied to the composite results in fine-scale spatial variations of field strength within the sample, which depend on the properties of the constituents, their volume fractions, and their microgeometries. We derived analytic expressions for the electric field strength heterogeneity implicit in commonly used dielectric effective medium models. Only two-phase, statistically isotropic, low-loss materials, e.g., ice, snow, minerals, and freshwater in the microwave UHF band are considered. The method applies to singly or biconnected phases. The results confirm the uniform field in the isolated phase of material lying on the Hashin–Shtrikman (HS) bounds; the continuous phase field variance increases with a decreasing volume fraction, approaching a well-defined limit as the fraction becomes vanishingly small. Expressions are found for field variance in higher-order composites of coated spheres, providing realizations of composites lying between the HS bounds, and illustrating field nonuniqueness when microstructure is unknown. The mean and variance of the field strength in popular effective medium models are also examined. Not only do the effective properties predicted by these models differ so do the electric field strength spatial variability, especially when the volume fraction of inclusions increases.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Non-Common Band SAR Interferometry Via Compressive Sensing
    • Authors: Huizhang Yang;Chengzhi Chen;Shengyao Chen;Feng Xi;Zhong Liu;
      Pages: 4436 - 4453
      Abstract: To avoid decorrelation, conventional synthetic aperture radar interferometry (InSAR) requires that interferometric images should have a common spectral band and the same resolution after proper preprocessing. For a high-resolution (HR) image and a low-resolution (LR) one, the interferogram quality is limited by the LR one since the non-common band (NCB) between two images is usually discarded. In this article, we try to establish an InSAR method to improve interferogram quality by means of exploiting the NCB. To this end, we first define a new interferogram, which has the same resolution as the HR image. Then we formulate the interferometric relationship between the two images into a compressive sensing (CS) model, which contains the proposed HR interferogram. With the sparsity of interferogram in appropriate domains, we model the interferogram formation as a typical sparse recovery problem. Due to the speckle effect in coherent radar imaging, the sensing matrix of our CS model is inherently random. We theoretically prove that the sensing matrix satisfies restricted isometry property, and thus the interferogram recovery performance is guaranteed. Furthermore, we provide a fast interferogram formation algorithm by exploiting computationally efficient structures of the sensing matrix. Numerical experiments show that the proposed method provides better interferogram quality in the sense of reduced phase noise and obtain extrapolated interferogram spectra with respect to CB processing.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Polarimetric SAR Calibration and Residual Error Estimation When Corner
           Reflectors Are Unavailable
    • Authors: Lei Shi;Pingxiang Li;Jie Yang;Liangpei Zhang;Xiaoli Ding;Lingli Zhao;
      Pages: 4454 - 4471
      Abstract: In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China’s newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Efficient Seismic Source Localization Using Simplified Gaussian Beam Time
           Reversal Imaging
    • Authors: Fangyu Li;Tong Bai;Nori Nakata;Bin Lyu;Wenzhan Song;
      Pages: 4472 - 4478
      Abstract: With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • One-Step Generalized Likelihood Ratio Test for Subpixel Target Detection
           in Hyperspectral Imaging
    • Authors: François Vincent;Olivier Besson;
      Pages: 4479 - 4489
      Abstract: One of the main objectives of hyperspectral image processing is to detect a given target among an unknown background. The standard data to conduct such detection is a reflectance map, where the spectral signatures of each pixel’s components, known as endmembers, are associated with their abundances in the pixel. Due to the low spatial resolution of most hyperspectral sensors, such a target occupies a fraction of the pixel. A widely used model in the case of subpixel targets is the replacement model. Among the vast number of possible detectors, algorithms matched to the replacement model are quite rare. One of the few examples is the finite target matched filter (MF), which is an adjustment of the well-known MF. In this article, we derive the exact generalized likelihood ratio test for this model. This new detector can be used both with a local covariance estimation window or a global one. It is shown to outperform the standard target detectors on real data, especially for small covariance estimation windows.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
  • Imagine a community hopeful for the future
    • Pages: 4490 - 4490
      Abstract: Advertisement.
      PubDate: June 2020
      Issue No: Vol. 58, No. 6 (2020)
       
 
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