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  Subjects -> ELECTRONICS (Total: 184 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 6)
Advances in Electronics     Open Access   (Followers: 79)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 318)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 36)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 47)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 267)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 106)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 86)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 93)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 51)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 3)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 195)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 97)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 67)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 70)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 56)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 20)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 40)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 70)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 46)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 58)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 25)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 13)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
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: 6)
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: 2)
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: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 12)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 25)
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: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Engineered Fibers and Fabrics     Open Access   (Followers: 2)
Journal of Field Robotics     Hybrid Journal   (Followers: 3)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 169)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 29)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Jurnal Teknologi Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 19)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Electrical & Electronic Engineering Journal     Open Access  
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
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: 54)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 9)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Similar Journals
Journal Cover
Geoscience and Remote Sensing, IEEE Transactions on
Journal Prestige (SJR): 2.649
Citation Impact (citeScore): 6
Number of Followers: 195  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0196-2892
Published by IEEE Homepage  [191 journals]
  • IEEE Transactions on Geoscience and Remote Sensing publication information
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing information for authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Presents GRSS instituional listings for this issue of the publication.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Polarimetric Interferometric SAR Change Detection Discrimination
    • Authors: R. Derek West;Robert M. Riley;
      Pages: 3091 - 3104
      Abstract: A coherent change detection (CCD) image, computed from a geometrically matched, temporally separated pair of complex-valued synthetic aperture radar (SAR) image sets, conveys the pixel-level equivalence between the two observations. Low-coherence values in a CCD image are typically due to either some physical change in the corresponding pixels or a low signal-to-noise observation. A CCD image does not directly convey the nature of the change that occurred to cause low coherence. In this paper, we introduce a mathematical framework for discriminating between different types of change within a CCD image. We utilize the extra degrees of freedom and information from polarimetric interferometric SAR (PolInSAR) data and PolInSAR processing techniques to define a 29-dimensional feature vector that contains information capable of discriminating between different types of change in a scene. We also propose two change-type discrimination functions that can be trained with feature vector training data and demonstrate change-type discrimination on an example image set for three different types of change. Furthermore, we also describe and characterize the performance of the two proposed change-type discrimination functions by way of receiver operating characteristic curves, confusion matrices, and pass matrices.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • RFI Mitigation for UWB Radar Via Hyperparameter-Free Sparse SPICE Methods
    • Authors: Jiaying Ren;Tianyi Zhang;Jian Li;Lam H. Nguyen;Petre Stoica;
      Pages: 3105 - 3118
      Abstract: Radio frequency interference (RFI) causes serious problems to ultrawideband (UWB) radar operations due to severely degrading radar imaging capability and target detection performance. This paper formulates proper data models and proposes novel methods for effective RFI mitigation. We first apply the single-snapshot Sparse Iterative Covariance-based Estimation (SPICE) algorithm to data from each pulse repetition interval for RFI mitigation and discuss the connection of SPICE to the $l_{1}$ -penalized least absolute deviation ( $l_{1}$ -PLAD) approach. Then, we devise a modified group SPICE algorithm and we prove that it is equivalent to a special case of the $l_{1,2}$ -PLAD method. The modified group SPICE algorithm can be applied to data from a coherent processing interval for effective RFI mitigation. Both the single-snapshot SPICE and the modified group SPICE methods simultaneously exploit the sparsity properties of both RFI spectrum and UWB radar target echoes. Unlike the existing sparsity-based RFI suppression methods, such as the robust principal component analysis algorithm, the proposed methods are hyperparameter-free and therefore easier to use in practical applications. Furthermore, the fast implementation of the SPICE methods is considered by exploiting the special structures of both single-snapshot and multiple-snapshot covariance matrices. Finally, the results obtained from applying the SPICE methods to simulated data as well as measured data collected by the U.S. Army Research Laboratory synthetic aperture radar system are presented to demonstrate the effectiveness of the proposed methods.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Seasonal Differences in Dielectric Properties of Dwarf Woody Tundra
           Vegetation in a Microwave Range
    • Authors: Andrei N. Romanov;Petr N. Ulanov;
      Pages: 3119 - 3125
      Abstract: It is vital to study seasonal variations in dielectric properties of woody tundra vegetation for the development of algorithms for satellite data interpretation and for the accuracy improvement of microwave methods for remote tundra sensing. This paper presents the measurement results of dielectric characteristics of dwarf birch (Betula nana) and willow (Salicaceae) at a frequency of 1.41 GHz. Vegetation samples were taken in the Yamal-Nenets Autonomous Okrug in different seasons (April, August, and November). Dielectric properties were measured by means of a bridge-type setup in the temperature range from 263 to 293 K at different water content in plant samples. Seasonal differences in temperature dependences of the real and imaginary parts of complex permittivity (CP), the loss-angle tangent were established for the studied tree species. These variations were caused by the water amount change in plants, the formation of ice in plant cells, the difference in dielectric properties of dry wood, cell sap, and ice. For branches and leaves, empirical dependences of CP on volume moisture were established. CP is higher in branch wood than that of in leaves due to greater volume of free water in the intercellular space of branch wood. Significant seasonal differences in woody tundra CP are indicative of the need for consideration of such seasonal changes under tundra remote sensing.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Ascending–Descending Bias Correction of Microwave Radiation Imager
           on Board FengYun-3C
    • Authors: Xinxin Xie;Shengli Wu;Hongxin Xu;Weimin Yu;Jiakai He;Songyan Gu;
      Pages: 3126 - 3134
      Abstract: Microwave radiation imager (MWRI) is regarded as one of the most important microwave payloads on board China FengYun-3C Meteorological Satellite. The instrument suffers from calibration anomalies and exhibits observation- background (O-B) calibration bias difference between the ascending and descending passes at all channels (hereinafter AD bias). The calibration bias difference of MWRI between ascending and descending orbits hampers data assimilation in the numerical weather predictions and reanalysis systems. This paper proposes a physical-based correction algorithm for MWRI calibration, following a brief introduction to the calibration process of the instrument. The relationship between the observed brightness temperatures and the physical temperature of the hot load reflector is established to mitigate the intrusion of the emissive hot reflector at all channels which was not accurately estimated in the previous calibration process. Before- and after-correction comparisons using one-year observations show that the AD bias is effectively reduced, i.e., from ~2 K before correction to less than 0.2 K after correction, when rectifying the emissivity of the hot reflector in the calibration equation, whereas the change in the mean values of MWRI radiance is negligible.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • 3-D Ionospheric Tomography Using Model Function in the Modified L-Curve
           Method
    • Authors: Sicheng Wang;Sixun Huang;Shuai Lu;Bing Yan;
      Pages: 3135 - 3147
      Abstract: Ionospheric tomography based on the observed total electron content along different satellite-receiver rays is a typically ill-posed inverse problem. Incorporating the electron density profiles data from COSMIC radio occultation technique and ground ionosondes, the Tikhonov regularization method is adopted to reconstruct the 3-D ionospheric electron density, and a regularization parameter is used to balance the weights between the prior (or background) information and real measurements. To determine the optimal regularization parameter, the model function in the modified L-curve method is used. This new method combines the advantages of the model function approach and L-curve criterion, and it has not only high accuracy, but also rapid convergence. To validate the effectiveness of this reconstruction algorithm, both the ideal test and real measurements test are carried out, and the results show that this algorithm can significantly improve the background model outputs.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A New Azimuth Ambiguity Suppression Algorithm for Surface Current
           Measurement in Coastal Waters and Rivers With Along-track InSAR
    • Authors: Baochang Liu;Yijun He;Yongkang Li;Heyang Duan;Xin Song;
      Pages: 3148 - 3165
      Abstract: We present a new algorithm for suppressing azimuth ambiguities when measuring surface currents with an along-track interferometric (ATI) synthetic aperture radar in heterogeneous scenes, such as coastal waters or rivers. The key of the proposed algorithm involves a careful analysis of a parameter called the eigenvalue spectrum entropy (EVSE), which is defined as the entropy of the eigenvalue spectrum of the ATI covariance matrix computed in the Doppler domain. The physical meaning of EVSE is that it serves as a descriptor for the degree to which an unambiguous signal component and an ambiguous one are mixed. With the help of EVSE, azimuth ambiguities can be suppressed. Simulation results demonstrate that as compared with the conventional ATI method without azimuth ambiguity suppression, the proposed algorithm allows for a pronounced improvement in the current measuring accuracy. Other advantages of the proposed algorithm lie in the fact that it is not only adaptive, due to its ability to automatically capture the useful Doppler band whose width and position may both vary for different radar and scene parameters, but it also needs a minimal number of user inputs, making it a quite attractive algorithm for routine implementation.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Estimates of the Precipitation Top Heights in Convective Systems Using
           Microwave Radiances
    • Authors: Francisco J. Tapiador;Raul Moreno;Ziad S. Haddad;
      Pages: 3166 - 3178
      Abstract: The absence of systematic estimates of vertical transport of water by convective storms is one of the main causes of uncertainties in weather forecasting in the tropics, in severe weather forecasting, and in the climate-scale analysis and prediction of global circulation. The difficulty of providing such estimates lies in that the only instruments capable of providing such estimates at present, radiometers onboard satellites, operate at short wavelengths, and that makes difficult the retrievals. This paper describes a method capable of providing robust estimates of the maximum height of precipitation and the 3-D structure of condensed water in convective systems using a constellation of radiometers. Using hurricane Winston and the Global Precipitation Measurement (GPM) mission Core Observatory satellite dual precipitation radar as a reference, it is shown that the method is suitable to improve the parameterization of convection in numerical models.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Scatterometer Backscatter Imaging Using Backus–Gilbert Inversion
    • Authors: David G. Long;
      Pages: 3179 - 3190
      Abstract: Wind scatterometer measurements are collected over an irregular grid, and processing is required to generate backscatter images on an Earth-centered grid. The most common algorithms used for this are “drop in the bucket” (DIB) and variations of the scatterometer image reconstruction (SIR) algorithm. These algorithms are also used for radiometer brightness temperature imaging. The Backus–Gilbert (BG) algorithm has been used for radiometer imaging but has not been applied to scatterometer backscatter imaging. In this paper, the application of BG to scatterometer backscatter imaging is explored and its performance is compared to DIB and SIR. Like SIR, optimally tuned BG is capable of producing higher resolution images than DIB, though its noise performance is slightly inferior to SIR’s. While BG and SIR produce similar results for radiometer data, the higher relative noise level of scatterometer data increases the differences between the SIR and BG algorithm performance, and limits the performance of BG relative to SIR in scatterometer imaging. Comparison of the SIR and BG algorithms in scatterometer imaging offers important insights into the inversion/reconstruction problem.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Development of an On-Board Wide-Band Processor for Radio Frequency
           Interference Detection and Filtering
    • Authors: Sidharth Misra;Jonathon Kocz;Robert Jarnot;Shannon T. Brown;Rudi Bendig;Carl Felten;Joel T. Johnson;
      Pages: 3191 - 3203
      Abstract: The demand for microwave spectrum for commercial and industrial use has been increasing rapidly over the last decade, putting stress on the limited spectral resources for passive microwave remote sensing. Radio frequency interference from man-made sources is expected to become worse over the coming years. At 1.4 GHz, the SMAP mission has implemented and demonstrated advanced interference detection algorithms for its microwave radiometer. This scheme will not be feasible at higher microwave frequencies (above 6 GHz) due to much larger radiometer bandwidths used and the limited downlink data volume available to implement RFI filtering algorithms in the ground processing. In this paper, we present the design, development, and test of an advanced on-board interference detection and RFI filtering digital back-end that is capable of operation for a 1 GHz-radiometer bandwidth. We describe the combined RFI detection algorithms implemented in the digital backend’s firmware and the on-board RFI filtering of interference-corrupted data that will be necessary to limit downlink rate requirements for future high-frequency microwave missions.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • On Gleaning Knowledge From Cross Domains by Sparse Subspace Correlation
           Analysis for Hyperspectral Image Classification
    • Authors: Xue Li;Liangpei Zhang;Bo Du;Lefei Zhang;
      Pages: 3204 - 3220
      Abstract: Despite the availability of an increasing amount of remote sensing images, problems still arise in that the knowledge from existing images is underutilized and the collection of reference knowledge for each newly obtained image is expensive. Recently, an attractive solution called “transfer learning” has received increasing attention in the remote sensing field, by transferring knowledge from source domains to help improve the learning procedure in the target domain. In this paper, we propose a sparse subspace correlation analysis-based supervised classification (SSCA-SC) method for transfer learning in hyperspectral remote sensing image classification, which is not restricted by the data dimensionality or the data acquisition sensors. Specifically, we first propose a sparse subspace correlation analysis (SSCA) method to simultaneously learn the optimal projection matrices for heterogeneous domains into a common subspace and obtain sparse reconstruction coefficients over a shared self-expressive dictionary in the derived subspace. In order to fully utilize the label information to improve the class separability, the SSCA-SC framework learns more discriminative representations for the input data by training a corresponding SSCA model for each class. As a result, the projected data belonging to the same class are maximally correlated and represented well, while those from different classes will have a low correlation. Another advantage of the SSCA-SC framework lies in the fact that it not only learns new representations for the data from different domains but it also designs a discriminative and robust classifier that properly adapts to the new representation. The proposed method was tested with three hyperspectral remote sensing data sets, and the experimental results confirm the effectiveness and reliability of the proposed SSCA-SC method.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Spectral Assignment-Oriented Approach to Improve Interpretability and
           Accuracy of Proxy Spectral-Based Models
    • Authors: Nimrod Carmon;Eyal Ben-Dor;
      Pages: 3221 - 3228
      Abstract: In modeling chemical attributes using hyperspectral data, nonlinear relationships between the predictor and the response are frequent. The common nonlinear modeling techniques improve prediction accuracy but suffer from low interpretability of the models. In this paper, we demonstrate a new multivariate modeling method, denoted as spectral assignment-oriented partial least squares (SAO-PLS), which is designed to provide a nonlinear modeling solution with strong interpretability products. The need for this approach is apparent when a given sample population consists of different spectral features for different levels of the response. Accordingly, the suggested SAO-PLS algorithm segments the data in an optimal location on the response distribution by maximizing the difference in spectral assignments between two clusters. SAO-PLS is applied here to two test cases with different characteristics: 1) an established data set containing airborne hyperspectral data of asphaltic roads, merged with in situ measured dynamic friction values captured using a standardized method and 2) a soil spectral library, spectrally measured with an analytical spectral device spectrometer, to which organic carbon measurements were applied. Our results demonstrate the superiority of SAO-PLS over partial least-squares regression for both model accuracy and interpretability, providing a deeper understanding of the underlying processes.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Ice Cloud Properties From Himawari-8/AHI Next-Generation Geostationary
           Satellite: Capability of the AHI to Monitor the DC Cloud Generation
           Process
    • Authors: Husi Letu;Takashi M. Nagao;Takashi Y. Nakajima;Jérôme Riedi;Hiroshi Ishimoto;Anthony J. Baran;Huazhe Shang;Miho Sekiguchi;Maki Kikuchi;
      Pages: 3229 - 3239
      Abstract: The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • FengYun-3 B Satellite Medium Resolution Spectral Imager Visible On-Board
           Calibrator Radiometric Output Degradation Analysis
    • Authors: Dandan Zhi;Wei Wei;Yanna Zhang;Tanqi Yu;Yan Pan;Ling Sun;Xin Li;Xiaobing Zheng;
      Pages: 3240 - 3251
      Abstract: The output of the FengYun-3 B (FY-3B) satellite’s medium resolution spectral imager (MERSI) visible on-board calibrator (OBC) degrades with time, causing concerns regarding its reliability in radiometric calibration. Special efforts have been made to extract the variation in information of the interior lamps. The specifications include: 1) digital count retrieval from the OBC files and an exponential plus linear fit applied to determine the relative change in the MERSI response; 2) anchoring to cross calibration with MODIS to acquire the absolute radiometric calibration coefficients; 3) a time series calibration tracking method based on the pseudoinvariant target site applied to monitor the degradation of the FY-3B MERSI instrument; and 4) “net” interior lamp radiometric output (ILRO) obtained by subtracting long-term pseudoinvariant tracking results from the scaled visible on-board calibrator-based model gain coefficients. The results show that the decay rates of the ILRO are wavelength dependent and the shortwave outputs are found to experience a large degradation, particularly at 470, 550, 412, 443, 490, 520, and 565 nm. The largest annual degradation rate is greater than 15% in the first year. The implementation of the current procedure combined with a detailed analysis will provide a basis for further determining lamp changes, leading to improved absolute calibration accuracy of MERSI products.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • An Automatic Method for Subglacial Lake Detection in Ice Sheet Radar
           Sounder Data
    • Authors: Ana-Maria Ilisei;Mahdi Khodadadzadeh;Adamo Ferro;Lorenzo Bruzzone;
      Pages: 3252 - 3270
      Abstract: During the past decades, radar sounder (RS) instruments have been effectively used to detect subglacial lakes (SLs). SLs appear as flat, smooth, and bright reflectors in RS radargrams. The visual interpretation has been the main approach to SL detection in radargrams. However, this approach is subjective and inappropriate for processing large amounts of radargrams. While the analysis of RS data for understanding the subglacial hydrology has recently received increased attention, the literature on the development of automatic methods specifically designed for SL detection is still limited. In order to fill this gap, in this paper, we propose a novel automatic technique for SL detection. The technique is made up of two steps: 1) feature extraction and 2) automatic detection. In the first step, we define and extract three families of features for discriminating between the lake and nonlake radar reflections. The features model locally the basal topography, the shape of the basal reflected waveforms, and the statistical properties of the basal signal. In the second step, we provide the features as input to a support vector machine classifier to perform the automatic SL detection. The proposed technique has been applied to radargrams acquired over two large regions in East Antarctica and Siple Coast. The obtained results, which are validated both quantitatively and qualitatively, confirm the robustness of the features and their capabilities to effectively characterize SLs. Moreover, they prove the potentiality of the method to process large amounts of radargrams and update the current SL inventory.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Adaptive Ground Clutter Reduction in Ground-Penetrating Radar Data Based
           on Principal Component Analysis
    • Authors: Gaoxiang Chen;Liyun Fu;Kanfu Chen;Cyril D. Boateng;Shuangcheng Ge;
      Pages: 3271 - 3282
      Abstract: Singular value decomposition is an effective way to remove ground clutter in ground-penetrating radar (GPR) applications. The main limitation of this method is the selection of principal components to completely reconstruct the ground clutter or the target. To date, no effective criteria or technology have been developed. To solve this problem, a new method is proposed in this paper. The research and analysis presented herein reveal that the root-mean-square height (RMSH) of the first-arrival curve corresponding to the ground clutter has a well-defined positive relationship with the number of singular values associated with the principal components of the ground clutter. The number of singular values of these principal components ( $N$ ) can be precisely determined based on the ground clutter by a linear function, $N = 0.2634D + 1.3086$ , where D represents the RMSH value. In addition, an algorithm called developed histogram equalization was developed to improve the contrast to highlight the targets in denoized GPR data sets. The proposed strategy of extracting the principal components of the ground clutter and highlighting the contrast between the target signal and environmental reflections was successfully applied to the field GPR data, thus demonstrating the practicality and validity of the proposed approach.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • SAR Ground Moving Target Imaging Based on a New Range Model Using a
           Modified Keystone Transform
    • Authors: Guanghu Jin;Zhen Dong;Feng He;Anxi Yu;
      Pages: 3283 - 3295
      Abstract: Imaging of a ground moving target (GMT) with synthetic aperture radar (SAR) is a challenging task, particularly when the Doppler ambiguity happens. This paper proposes a novel GMT range model and a novel Doppler ambiguity-tolerated GMT imaging algorithm based on modified keystone transform. In the proposed GMT range model, the range from radar antenna to the scattering center on the target is divided into two parts: the range from radar antenna to the target centroid and the projection length on the light of sight from the target centroid to the scattering center. Based on the new range model, a Doppler ambiguity-tolerated range cell migration correction (RCMC) method is proposed, in which keystone transform is modified to realize the differential RCMC by interpolation with nonzero phase sinc kernel. Finally, the GMT image is obtained in the range-Doppler domain by matched filtering in the slow-time domain and is restored into the 2-D space domain. The effectiveness of our proposed model and imaging method is demonstrated by both simulated and real airborne SAR data.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Single-Pulse Mueller Matrix LiDAR Polarimeter: Modeling and Demonstration
    • Authors: Christian K. Keyser;Richard K. Martin;P. Khanh Nguyen;Arielle M. Adams;
      Pages: 3296 - 3307
      Abstract: We introduce a new LiDAR polarimeter architecture enabling measurement of a large subset of a target Mueller matrix in a single ~10-ns laser pulse; this may be beneficial for remote sensing from moving platforms in terrestrial and defense applications. We will numerically explore the measurement accuracy dependence on various hardware parameters and signal-to-noise ratio and provide an experimental demonstration. For the transmitter, we direct the laser pulse through an electrooptic (EO) phase modulator wherein a high-voltage ramp is synchronously applied. As the laser pulse propagates through the crystal, the voltage ramp induces a time-varying birefringence which results in a well-defined time-varying polarization across the laser pulse temporal envelope used to illuminate the target. The architecture can measure as many as 12 elements of target Mueller matrix by employing three polarization state analyzers. Knowing the temporal distribution of the transmitted polarization signal and employing fast detectors in the polarization analyzers, the Mueller matrix elements are estimated. We will introduce a model describing the transmitted and received signal polarization temporal distribution and show how Mueller matrix elements can be obtained. We will demonstrate the concept using a 10-ns laser pulse, an EO phase retarder in the transmitter and using two polarization analyzers in the receiver to measure nine elements of the Mueller matrix in a point and shoot configuration. Measurements will be compared to a theoretical reference to assess accuracy.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Framework for Global Characterization of Soil Properties Using Repeat
           Hyperspectral Satellite Data
    • Authors: Debsunder Dutta;Praveen Kumar;
      Pages: 3308 - 3323
      Abstract: Imaging spectroscopy offers the potential to quantify the soil properties over large areas based on its reflectance spectra. Soils are heterogeneous mixtures of minerals, air, water, and organic matter leading to complex manifestations of reflectance in the different parts of the visible-shortwave infrared spectra. Due to this complexity, data-driven modeling approaches are found to be most suitable for characterizing the relationships between soil spectra and the corresponding soil properties. Proposed spaceborne hyperspectral missions, such as Hyperspectral Infrared Imager, offer the possibility of repeating global spectral measurements in a 16- to 20-day revisit period. Soil attributes on the landscape vary at different rates. In particular, the soil textural attributes (percentage of sand, silt, and clay) may be assumed to remain invariant compared to chemical constituents during multiple consecutive 16- to 20-day satellite revisit period. We present a theoretical retrieval framework for assimilating repeat spaceborne soil spectral measurements into a previously developed lasso algorithm-based ensemble modeling framework for the global-scale characterization of soil textural attributes. The repeat spectral assimilation with each overpass of the satellite leads to the development of an enriched “dynamic soil spectral library” which spatially propagates the improvement in the characterization of soil textural properties globally, given the uncertain variations in other auxiliary factors, such as moisture and organic matter, affecting soil reflectance.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Simple Approximation for the Reflectance of a Thick Cloud in Gaseous
           Absorption Band and Its Application for the Cloud-Top Height Determination
           
    • Authors: Alexander A. Kokhanovsky;Luca Lelli;Fabrice Ducos;Rosemary Munro;
      Pages: 3324 - 3330
      Abstract: This paper presents a new, simple, fast method to retrieve cloud altitude from measurements of light absorption in the strongest molecular band of oxygen (the A-band), centered at 761 nm, by the polarization and directionality of the earth’s reflectance sensor. First, we assess the validity of the method against synthetic spectra as a function of realistic cloud scenarios and satellite observation geometries. The retrieval error estimate amounts, on average, to less than 500 m. Second, the hurricane Ileana, overpassed the August 30, 2012, off the coasts west of Mexico and Southern California, is selected as test bed for the comparison of our retrievals against independent and nearly coincident cloud data inferred with the Moderate Resolution Imaging Spectroradiometer onboard Aqua and the Global Ozone Monitoring Experiment 2 onboard MetOp-A. We find that our approach can accurately reproduce the cloud height patterns, as long as the clouds are not thin cirrus.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Novel Azimuth Cutoff Implementation to Retrieve Sea Surface Wind Speed
           From SAR Imagery
    • Authors: Valeria Corcione;Giuseppe Grieco;Marcos Portabella;Ferdinando Nunziata;Maurizio Migliaccio;
      Pages: 3331 - 3340
      Abstract: In this paper, the synthetic aperture radar (SAR) azimuth cutoff method is thoroughly revised and a new and general implementation is proposed. The key roles of the pixel spacing, the size of the image box, and the texture of the SAR scene are analyzed and optimized in terms of azimuth cutoff ( $lambda _{mathrm{ c}}$ ) estimation. The reliability of the $lambda _{mathrm{ c}}$ estimation is analyzed by measuring the distance between the measured and fitted autocorrelation functions. This analysis shows that it is of paramount importance to filter unfeasible/unreliable $lambda _{mathrm{ c}}$ values. To identify those values in an objective way, a criterion that is based on the $chi _{2}$ test performed over a large data set of Sentinel-1 SAR imagery is defined and proven to be effective. The new robust implementation of the $lambda _{mathrm{ c}}$ estimation at about 1-km grid spacing is then used to produce averaged $lambda _{mathrm{ c}}$ at about 10-km grid spacing. The performance of the new estimation procedure, analyzed using a $lambda _{mathrm{ c}}$ -to-wind-speed forward model, is shown to provide improved wind speed retrievals, with a root-mean-square error of 1.8–2 m/s when verified against independent numerical weather prediction model output and scatterometer winds.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Dense Stereo Matching Based on Multiobjective Fitness Function—A Genetic
           Algorithm Optimization Approach for Stereo Correspondence
    • Authors: Manimala Mahato;Shirishkumar Gedam;Jyoti Joglekar;Krishna Mohan Buddhiraju;
      Pages: 3341 - 3353
      Abstract: Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Mixed Total Field/Scattered Field-Based Discontinuous Galerkin
           Frequency-Domain Method for Subsurface Sensing
    • Authors: Qingtao Sun;Qiwei Zhan;Runren Zhang;Qing Huo Liu;
      Pages: 3354 - 3360
      Abstract: To model the responses of electromagnetic surveys for geophysical subsurface sensing, a mixed total field/scattered field-based discontinuous Galerkin frequency-domain (TF/SF DGFD) method is proposed in this paper. The proposed TF/SF DGFD method is implemented at a subdomain level based on the domain decomposition technique. Different subdomains can employ either the TF DGFD framework or the SF DGFD framework, which are then coupled through the Riemann transmission condition. To balance the computation efficiency and accuracy for practical applications, the proposed method prefers to using the SF DGFD framework for subdomains with sources and using the TF DGFD framework for the remaining subdomains. At the interfaces between total field and scattered field subdomains, the Riemann transmission condition is slightly modified by incorporating the background fields due to the physically imposed sources in the background media. In this way, the proposed method only requires surface integrals of the background fields as extra overhead instead of elementwise integration of the scattering objects for the purely scattered field-based method, which can improve the computational efficiency. Also, it is more accurate than the purely TF DGFD method given the same mesh. Numerical examples are studied to examine the performance of the proposed method, which is proven to have better accuracy than the TF DGFD method. The TF/SF DGFD method will facilitate modeling of electromagnetic surveys under complicated geophysical environments for subsurface sensing.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Gaussian Process Regression for Forest Attribute Estimation From Airborne
           Laser Scanning Data
    • Authors: Petri Varvia;Timo Lähivaara;Matti Maltamo;Petteri Packalen;Aku Seppänen;
      Pages: 3361 - 3369
      Abstract: While the analysis of airborne laser scanning (ALS) data often provides reliable estimates for certain forest stand attributes-such as total volume or basal area-there is still room for improvement, especially in estimating species-specific attributes. Moreover, while the information on the estimate uncertainty would be useful in various economic and environmental analyses on forests, a computationally feasible framework for uncertainty quantifying in ALS is still missing. In this paper, the species-specific stand attribute estimation and uncertainty quantification (UQ) is approached using Gaussian process regression (GPR), which is a nonlinear and nonparametric machine learning method. Multiple species-specific stand attributes are estimated simultaneously: tree height, stem diameter, stem number, basal area, and stem volume. The cross-validation results show that GPR yields on average an improvement of 4.6% in estimate root mean square error over a state-of-the-art $k$ -nearest neighbors (kNNs) implementation, negligible bias and well performing UQ (credible intervals), while being computationally fast. The performance advantage over kNN and the feasibility of credible intervals persists even when smaller training sets are used.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Improved Ultrahigh-Resolution Wind Retrieval for RapidScat
    • Authors: Nolan Hutchings;David G. Long;
      Pages: 3370 - 3379
      Abstract: This paper introduces RapidScat 2.5-km ultrahigh-resolution (UHR) wind estimation and validates it in near-coastal regions. RapidScat UHR wind estimation provides finer resolution ocean wind vector fields than conventional 12.5-km level 2B (L2B) wind products at a cost of higher noise. In addition, this paper applies direction interval retrieval techniques and develops other wind processing improvements to enhance the performance of RapidScat UHR wind estimation. The new algorithm is validated with L2B wind estimates, numerical weather prediction wind products, and buoy measurements. The wind processing improvements produce more spatially consistent UHR winds that compare well with the wind products mentioned above.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • MIMO Borehole Radar Imaging Based on High Degree of Freedom for Efficient
           Subsurface Sensing
    • Authors: Na Li;Haining Yang;Tingjun Li;Yong Fan;Qing Huo Liu;
      Pages: 3380 - 3391
      Abstract: This paper presents an efficient multiple-input multiple-output (MIMO) borehole radar imaging method based on a high degree of freedom for subsurface sensing. The variable separations between the different transmitter and receiver pairs in MIMO borehole radar are considered and introduced as imaging coefficients into the expanded unified MIMO sample set, which gives the MIMO sample set desired characteristics of a high degree of freedom. By exploiting the high degree of freedom in the unified MIMO expanded sample set, the sample interpolation and energy migration of target reflections can be done in once efficient imaging processing for all transmitters of the MIMO radar system, and finally, accurate target image with low sidelobe level (SL) can be delivered. The computational cost of the proposed method will barely increase with the number of transmitters in the MIMO radar survey, which enables the proposed method to handle MIMO borehole radar imaging in an accurate and efficient manner. The imaging properties of the proposed method are proven to be superior to the conventional imaging methods in SL and computational cost, which is suitable for large MIMO borehole imaging surveys in subsurface sensing applications.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Novel High-Precision Range Estimation Method Based on Phase of Wideband
           Radar Echo
    • Authors: Xiangyu Xiong;Zhenmiao Deng;Wei Qi;Hai Ou;Zijian Cui;
      Pages: 3392 - 3403
      Abstract: High-precision parameter estimation is crucial for micromotion feature acquisitions. A method of high-precision range estimation based on phase of wideband radar echo is proposed in this paper. After the proposed processing, unwrapped signal phases without any ambiguity can be obtained to calculate the relative range of the target. No constraint condition for phase ambiguity resolution is imposed on the proposed method, which means that it could still work well in low signal-to-noise ratio situations. Cramér–Rao lower bound for the root-mean-squared error of the range estimation is derived in analytical expression. Results of several simulations and experiment with FEKO generated data are presented to show the effectiveness and antinoise performance of the proposed method.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Precise Registration of Laser Mapping Data by Planar Feature Extraction
           for Deformation Monitoring
    • Authors: Arpan Kusari;Craig L. Glennie;Benjamin A. Brooks;Todd L. Ericksen;
      Pages: 3404 - 3422
      Abstract: Quantifying near-field displacements can help enable a better understanding of earthquake physics and hazards. To date, established remote sensing techniques have failed to recover subcentimeter-level near-field displacements at the scale and resolution required for shallow fault physical investigations. In this paper, methods are developed to rapidly extract planar parameters, using fast parallel approaches and an alternative registration approach is employed to automatically match the planes extracted from pairwise temporally spaced mobile laser scanning (MLS) and Airborne laser scanning (ALS) data sets along the Napa fault. The features extracted from two temporally spaced point clouds are then used to calculate rigid-body transformation parameters. The production of robust and accurate deformation maps requires the selection of appropriate planar feature extraction and feature mapping criteria. Rigorously propagated point accuracy estimates are employed to produce realistic estimated errors for the transformation parameters. Displacements of each aggregate study area are computed separately from left and right sides of the fault and compared to be within 3 mm of alinement array displacements. Local differential displacements show distinct patterns which, compared to alinement array measurements, were found to agree within the confidence bounds. The findings demonstrate the ability to accurately estimate near-field deformations from repeated MLS or ALS scans of earthquake-prone urban areas. ALS is also used in conjunction with the MLS data sets, illustrating the algorithm’s ability to accommodate different LiDAR collection modalities at subcentimeter-level accuracy. The automated planar extraction and registration is an important contribution to the study of near-field earthquake dynamics and can be used as input observations for future geological inversion models.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Semisupervised Spatial Spectral Regularized Manifold Local Scaling Cut
           With HGF for Dimensionality Reduction of Hyperspectral Images
    • Authors: Ramanarayan Mohanty;S. L. Happy;Aurobinda Routray;
      Pages: 3423 - 3435
      Abstract: Hyperspectral images (HSIs) contain a wealth of information over hundreds of contiguous spectral bands, making it possible to classify materials through subtle spectral discrepancies. However, the classification of this rich spectral information is accompanied by the challenges like high dimensionality, singularity, limited training samples, lack of labeled data samples, heteroscedasticity, and nonlinearity. To address these challenges, we propose a semisupervised graph-based dimensionality reduction (DR) method named “semisupervised spatial spectral regularized manifold local scaling cut” (S3RMLSC). The underlying idea of the proposed method is to exploit the limited labeled information from both the spectral and spatial domains along with the abundant unlabeled samples to facilitate the classification task by retaining the original distribution of the data. In S3RMLSC, a hierarchical guided filter is initially used to smoothen the pixels of the HSI data to preserve the spatial pixel consistency. This step is followed by the construction of linear patches from the nonlinear manifold by using the maximal linear patch criterion. Then, the interpatch and intrapatch dissimilarity matrices are constructed in both spectral and spatial domains by RMLSC and neighboring pixel MLSC, respectively. Finally, we obtain the projection matrix by optimizing the updated semisupervised spatial–spectral between-patch and total-patch dissimilarity. The effectiveness of the proposed DR algorithm is illustrated with publicly available real-world HSI data sets.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Refocusing FMCW SAR Moving Target Data in the Wavenumber Domain
    • Authors: Emiliano Casalini;Max Frioud;David Small;Daniel Henke;
      Pages: 3436 - 3449
      Abstract: Frequency-modulated continuous-wave (FMCW) synthetic aperture radar (SAR) is now a commonly adopted solution for producing high-resolution electromagnetic images of the earth surface. However, many SAR applications, such as detection or imaging of moving targets, have not yet been thoroughly researched for FMCW scenarios. This paper presents a refocusing technique that works in the wavenumber domain and is based on the reference spectrum of a point-like moving target. The method is illustrated using simulated experiments, and is further tested by processing real data collected by the Ka-band MIRANDA-35 sensor. The obtained results are validated and compared with independent ground measurements, thus demonstrating the accuracy of the presented method.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Migratory Insect Multifrequency Radar Cross Sections for Morphological
           Parameter Estimation
    • Authors: Rui Wang;Cheng Hu;Changjiang Liu;Teng Long;Shaoyang Kong;Tianjiao Lang;Philip J. L. Gould;Jason Lim;Kongming Wu;
      Pages: 3450 - 3461
      Abstract: Insect migration provides major ecosystem services, and sometimes, migratory pests cause serious crop damage and yield loss. Species identification is critically important in studies of insect migration, for both entomologists and pest managers. Radar is an effective means of detecting insect migrants. Current entomological radars usually operate at X-band, and signal amplitude information is used to estimate body mass and wing-beat frequency, which can then be used to categorize migratory insects into broad taxon classes. To improve the identification performance, this paper presents a novel radar method of measuring insect mass and body length. The multifrequency radar cross sections (RCS) of insects at X-band and Ku-/K-band are fully investigated, and the comprehensive relationship between RCS and insect morphological parameters provides an improvement in the estimation of insect mass. More importantly, estimations of body length can also be realized with an accuracy of 84% based on experimental data acquired by a vector network analyzer in a microwave anechoic chamber. If multifrequency RCS measurements can be obtained by radar in the future, then highly accurate estimations of insect mass and body length will be possible, although it is currently still a challenge to build a radar capable of making the required measurements over such a wide frequency range.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Multispectral Airborne LiDAR Data in the Prediction of Boreal Tree Species
           Composition
    • Authors: Mikko Kukkonen;Matti Maltamo;Lauri Korhonen;Petteri Packalen;
      Pages: 3462 - 3471
      Abstract: Multispectral light detection and ranging (LiDAR) instruments, such as Optech Titan, record intensities at multiple wavelengths and these intensities can be used for tree species prediction in the same way as multispectral image data. In this paper, our main objective was to compare the accuracy of tree species prediction in a boreal forest area using multispectral LiDAR, the 1064-nm wavelength channel (“unispectral LiDAR”), and unispectral LiDAR with auxiliary aerial image data. We also evaluated the effect of the widely used intensity range correction method. We classified the main tree species of field plots using linear discriminant analysis (LDA) and predicted the species-specific volume proportions (%) for Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and broadleaved trees using the $k$ -nearest neighbor imputation. The effect of intensity correction on prediction errors for the dominant tree species was evaluated using optimal parameters derived from: 1) minimal intensity difference between flight lines; 2) parameters suggested by theory; and 3) uncorrected data. Although the range correction increased the classification accuracy slightly, it was observed to be ambiguous, and not consistent with theory for canopy echoes. Regardless, the intensity values were useful for the prediction of dominant tree species and species’ volume proportions. The results for the dominant tree species classification using multispectral LiDAR [overall accuracy (OA) 88.2%, kappa 0.79] were comparable to the use of unispectral LiDAR and aerial images (OA 89.1%, kappa 0.81). We conclude that the multispectral LiDAR may become a useful tool in operational species-specific forest inventories.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • SAR Image Despeckling Based on Nonlocal Low-Rank Regularization
    • Authors: Dongdong Guan;Deliang Xiang;Xiaoan Tang;Gangyao Kuang;
      Pages: 3472 - 3489
      Abstract: In this paper, we propose a new synthetic aperture radar (SAR) image despeckling method based on the nonlocal low-rank minimization model. First, some similar image patches are selected for each pixel to construct the patch group matrix (PGM). Then, a new low-rank minimization model, called Fisher–Tippett distribution (FT)-weighted nuclear norm minimization (WNNM), is proposed to recover the underlying low-rank component from the PGM. Specifically, the FT-WNNM is developed by reformulating the despeckling problem as the maximizing a posterior probability problem. The new model consists of a data fidelity term and a regularization term (also called prior term). The data fidelity term is derived from the statistical distribution of SAR images in the logarithm domain, which is known as the Fisher–Tippett distribution, and the regularization term is the recent weighted nuclear norm. Then, the alternating direction method of multipliers (ADMM) is introduced to solve the corresponding optimization problem. Under ADMM framework, the resulting subproblems can be solved efficiently and the convergence can be guaranteed. Extensive experiments on both simulated and real SAR images demonstrate that the proposed method can achieve comparable or even better despeckling performance than some state-of-the-art despeckling algorithms.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Calibration, Level 1 Processing, and Radiometric Validation for TANSO-FTS
           TIR on GOSAT
    • Authors: Fumie Kataoka;Robert O. Knuteson;Akihiko Kuze;Kei Shiomi;Hiroshi Suto;Jun Yoshida;Shinpei Kondoh;Naoko Saitoh;
      Pages: 3490 - 3500
      Abstract: The Greenhouse gases Observing SATellite (GOSAT) carries the Thermal and Near-Infrared Sensor for carbon Observations Fourier Transform Spectrometer (TANSO-FTS). TANSO-FTS covers a wide spectral range from the shortwave infrared to the thermal infrared (TIR). This paper describes the updated calibration algorithm of the TANSO-FTS Level 1B TIR spectra and the radiometric validation of the new V210.210 products by comparison with the previous version of V201.202. The revised nonlinearity correction for V210.210 product creates a decade-long, well-calibrated radiance set while minimizing the effect of two major anomalies: rotation stop of the one of the solar paddles in 2014 and a cryocooler shutdown in 2015, which caused abrupt changes in the thermal environment of the TANSO-FTS sensor. To check the improved nonlinearity correction and onboard calibration in TANSO-FTS V210.210 processing, we validated the entire spectral range by comparing with aircraft-based Scanning High-resolution Interferometer Sounder coincident in time with the GOSAT overpass. Also selected CO2 and CH4 channels are validated with the Atmospheric infrared sounder and the window channel with the in situ SST Quality Monitor data at temporally coincident and spatially collocated points. We have confirmed that the new V210.210 products exhibit no significant time trend in the window channel and a reduced spectral bias in the CO2 and CH4 channels. There remains some spectral bias, especially in the CO $_{2} nu _{2}$ channel and CH4 channel, which are attributed to the uncertainty of orbital and seasonal variations in the average direct current level of TANSO-FTS without photon input.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Automated Detection of Lunar Rockfalls Using a Convolutional Neural
           Network
    • Authors: Valentin Tertius Bickel;Charis Lanaras;Andrea Manconi;Simon Loew;Urs Mall;
      Pages: 3501 - 3511
      Abstract: This paper implements a novel approach to automatically detect and classify rockfalls in Lunar Reconnaissance Orbiter narrow angle camera (NAC) images using a single-stage dense object detector (RetinaNet). The convolutional neural network has been trained with a data set of 2932 original rockfall images. In order to avoid overfitting, the initial training data set has been augmented during training using random image rotation, scaling, and flipping. Testing images have been labelled by human operators and have been used for RetinaNet performance evaluation. Testing shows that RetinaNet is capable to reach recall values between 0.98 and 0.39, precision values between 1 and 0.25, and average precisions ranging from 0.89 to 0.69, depending on the used confidence threshold and intersection-over-union values. Mean processing time of a single NAC image in RetinaNet is around 10 s using a GeForce GTX 1080 Ti and GeForce Titan Xp, which is in orders of magnitudes faster than a human operator. The processing speed allows to efficiently exploit the currently available NAC data stack with more than 1 million images in a reasonable timeframe. The combination of speed and detection performance can be used to produce lunar rockfall distribution maps on large spatial scales for utilization by the scientific and engineering community.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform
           Combined With the Recursive Res-Net
    • Authors: Wen Ma;Zongxu Pan;Jiayi Guo;Bin Lei;
      Pages: 3512 - 3527
      Abstract: Deep learning (DL) has been successfully applied to single image super-resolution (SISR), which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) counterpart. Different from most current DL-based methods, which perform reconstruction in the spatial domain, we use a scheme based in the frequency domain to reconstruct the HR image at various frequency bands. Further, we propose a method that incorporates the wavelet transform (WT) and the recursive Res-Net. The WT is applied to the LR image to divide it into various frequency components. Then, an elaborately designed network with recursive residual blocks is used to predict high-frequency components. Finally, the reconstructed image is obtained via the inverse WT. This paper has three main contributions: 1) an SISR scheme based on the frequency domain is proposed under a DL framework to fully exploit the potential to depict images at different frequency bands; 2) recursive block and residual learning in global and local manners are adopted to ease the training of the deep network, and the batch normalization layer is removed to increase the flexibility of the network, save memory, and promote speed; and 3) the low-frequency wavelet component is replaced by an LR image with more details to further improve performance. To validate the effectiveness of the proposed method, extensive experiments are performed using the NWPU-RESISC45 data set, and the results demonstrate that the proposed method outperforms several state-of-the-art methods in terms of both objective evaluation and subjective perspective.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Long-Time Coherent Integration Algorithm for Radar Maneuvering Weak Target
           With Acceleration Rate
    • Authors: Penghui Huang;Xiang-Gen Xia;Guisheng Liao;Zhiwei Yang;Yuhong Zhang;
      Pages: 3528 - 3542
      Abstract: In this paper, we propose a long-time coherent integration method for a maneuvering target with the first-, second-, and third-order range migrations, and the complex Doppler frequency migration. In this method, after range compression, the echo signal is first transformed into the range-frequency and Doppler domain based on series reversion, and then an azimuth matched filtering procedure is implemented in the 2-D frequency domain. It can eliminate the coupling effects between range and azimuth jointly caused by the radial velocity, radial acceleration, and radial acceleration rate of a moving target. Due to the linear transform property, the proposed method can work well under low signal-to-clutter and noise ratio. In addition, the Doppler ambiguities, when target azimuth spectrum either is within a pulse repetition frequency (PRF) or spans over neighboring PRF bands, can be well solved. Both simulated and real synthetic aperture radar data processing results are provided to validate the effectiveness of the proposed algorithm.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Sensitivity Analysis of Multifrequency MIMP SAR Data From Rice Paddies
    • Authors: Motofumi Arii;Hiroyoshi Yamada;Shoichiro Kojima;Masato Ohki;
      Pages: 3543 - 3551
      Abstract: The determination of the accurate composition ratio of scattering mechanisms (volume scattering, double-bounce scattering, and surface scattering) within a radar backscatter is essential to validate current polarimetric decomposition techniques. Multiincidence angle and multipolarimetric synthetic aperture radar (MIMP SAR) observations at the X- and L-bands were applied to rice paddies at late vegetative stage in Niigata City in Japan in 2014 and 2016, respectively. In this paper, multifrequency MIMP SAR analysis is introduced based on the observation results. The approach, combined with theoretical characterization of the data by a discrete scatterer model, showed that rice panicles affect the backscatter from rice paddies. Contrary to expectation, an effect of transmissivity by using different bands is not obvious. The similar level of copolarization (HH and VV) backscatter at X- and L-bands could be explained by the effective size of rice panicles. They are the most characteristic scatters in rice paddy field with respect to multiple frequency polarimetric sensing. In addition, HV shows a distinct sensitivity to the mean orientation angle and the size of panicles regardless of the wavelength. The mean orientation angle affects the polarimetric randomness under azimuthal symmetry, whereas the size of panicles directly affects the attenuation of the volume scattering from the grains. The multifrequency MIMP SAR analysis also indicated the importance of considering the backscatter and attenuation in the interpretation of the backscattering cross section from vegetated fields.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Ratio-Based Multitemporal SAR Images Denoising: RABASAR
    • Authors: Weiying Zhao;Charles-Alban Deledalle;Loïc Denis;Henri Maître;Jean-Marie Nicolas;Florence Tupin;
      Pages: 3552 - 3565
      Abstract: In this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multitemporal mean. The proposed approach can be divided into three steps: 1) estimation of a “superimage” by temporal averaging and possibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “superimage”; and 3) computation of the denoised image by remultiplying the denoised ratio by the “superimage.” Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reduction method is more effective than denoising images from the original multitemporal stack. The amount of data that is jointly processed is also reduced compared to other methods through the use of the “superimage” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio and structure similarity index) as well as visually on simulated and synthetic aperture radar (SAR) time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Removal of the MCSST MODIS SST Bias During Upwelling Events Along the
           Southeastern Coast of Brazil
    • Authors: Gilberto R. Pimentel;Gutemberg B. França;Leonardo F. Peres;
      Pages: 3566 - 3573
      Abstract: Remotely sensed sea-surface temperature (SST) retrievals with a significant positive bias during the occurrence of upwelling phenomena along the southeastern coast of Brazil were reported in our companion paper. As a result, this paper proposes an automated bias correction algorithm to improve the MODIS long-wave multichannel SST (MCSST) retrievals during the abovementioned conditions in this region. In this paper, MODIS daytime SST data (SSTMODIS) and differences between brightness temperatures in MODIS channels 31 and 32 (BT31 - BT32) are analyzed simultaneously with hourly wind surface conditions, bfin situ SST at 0.3 and 10 m in depth (SSTbuoy03 and SSTbuoy10), and sensible and latent heat fluxes from the Cabo Frio buoy data (at 23° S, 42° W) during 2014. The obtained results show that some upwelling events present air temperature (Tair) greater than SSTbuoy03 and low-atmospheric water vapor content. A simultaneous occurrence of these factors during upwelling conditions may lead to a warm-skin layer effect and may cause BT31 to be greater than SSTbuoy03 and BT31 - BT32 to be small (-0.18 °C ± 0.22 °C), affecting the MCSST performance. The proposed bias correction algorithm uses a least-squares curve between SSTbuoy03 and SSTMODIS retrievals when BT31 - BT32 łe 0.5 °C (i.e., dry atmospheric conditions). The bias correction algorithm has significantly improved the SSTMODIS bias (RMSE) from 1.43 °C to -0.02 °C (1.60 °C to 0.58 °C) when applied to 22 cloud-free pixels of MODIS during January-March of 2015.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Impact of the Atmospheric Non-tidal Pressure Loading on Global Geodetic
           Parameters Based on Satellite Laser Ranging to GNSS
    • Authors: Grzegorz Bury;Krzysztof Sośnica;Radosław Zajdel;
      Pages: 3574 - 3590
      Abstract: Atmospheric pressure loading plays a crucial role in precise space geodesy, thus its omission, especially the nontidal loading (ANTL) part, leads to the inconsistency between solutions based on the microwave [Global Navigation Satellite Systems (GNSS)] and optical [satellite laser ranging (SLR)] observations. SLR observations are performed only during the cloudless conditions coincident with high values of air pressure that deforms the earth’s crust downward. The systematic shift of the estimated SLR station coordinates, which arises from the ANTL omission, is called the Blue-Sky effect. The offset is related to the long-term averaging of ANTL for SLR observations which are provided in sparse intervals, unlike GNSS, which observes continuously. Based on the ANTL model applied on SLR observations to GNSS, we determined the values of the Blue-Sky effect. The largest values of the Blue-Sky effect are observed for the inland stations, i.e., 2.3, 2.1, 2.0, and 1.9 mm for Svetloe, Potsdam, Baikonur, and Altay, respectively. We also investigate the impact of the omission of ANTL corrections on geodetic parameters, i.e., earth rotation parameters, geocenter motion, precise GNSS orbits, as well as on the global SLR network. The negligence of ANTL causes a systematic effect on geocenter coordinates with the amplitude of the annual signal at the level of 1.9 mm for the Z-component. The omission of ANTL corrections causes also a systematic shift of the multi-GNSS orbit constellation with the amplitude of the annual signal at the level of 2.7 mm for the Z-component.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Investigation of Sentinel-2 Bidirectional Reflectance Hot-Spot Sensing
           Conditions
    • Authors: Zhongbin Li;Hankui K. Zhang;David P. Roy;
      Pages: 3591 - 3598
      Abstract: Directional reflectance effects, often described by the bidirectional reflectance distribution function (BRDF), occur in Sentinel-2 multispectral instrument reflectance. The bidirectional hot-spot is a special case of the BRDF used to describe the increased backscatter reflectance that occurs over most surfaces when the solar and viewing directions coincide. A global year of Sentinel-2A metadata extracted using the Committee on Earth Observation Satellite Visualization Environment (COVE) tool and an established astronomical model were used to quantify the range of solar geometry and scattering angles expected in Sentinel-2A data. The established astronomical model was adapted to be Sentinel-2A specific and was parameterized as a function of the sensor acquisition date and nadir latitude. Solar zenith angles varied from 15.335° to 91.454°, and the scattering angles varied from 84.714° to 173.967°. To confirm the global COVE results, the scattering angles of a sample of Sentinel-2A data were examined and differed by less than 0.17° with respect to the COVE data. Given that hot-spots are only apparent when the scattering angle is close to 180°, we conclude that hot-spot will not occur in Sentinel-2A data. Equations and software to predict the scattering angle at the Sentinel-2A swath edge as a function of acquisition date and nadir latitude are provided so users may obtain data over a range of scattering angles in support of their BRDF studies.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A CNN With Multiscale Convolution and Diversified Metric for Hyperspectral
           Image Classification
    • Authors: Zhiqiang Gong;Ping Zhong;Yang Yu;Weidong Hu;Shutao Li;
      Pages: 3599 - 3618
      Abstract: Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Effects of Ice Particle Representation on Passive Microwave Precipitation
           Retrieval in a Bayesian Scheme
    • Authors: Sarah Ringerud;Mark S. Kulie;David L. Randel;Gail M. Skofronick-Jackson;Christian D. Kummerow;
      Pages: 3619 - 3632
      Abstract: A physically based Bayesian passive microwave precipitation retrieval requires an accurate forward radiative transfer model along with realistic database representation of hydrometeors, atmospheric properties, and surface emission. NASA’s Global Precipitation Measurement (GPM) Mission provides an unprecedented opportunity for the development of such databases, matching a well-calibrated radiometer with dual-frequency radar. Early versions of passive microwave products from GPM utilized a physically constructed database in a Bayesian retrieval scheme, assumed ice particles to be spheres, and used Mie radiative transfer. A large body of recent work demonstrates that this is insufficient for retrieval at the GPM radiometer frequencies. In this paper, the retrieval is updated to use nonspherical particles. Simulated brightness temperature (Tb) agreement with observations is shown to be significantly improved across the high frequencies, decreasing biases significantly and increasing correlations to observed Tb. This is compared with a second identical retrieval performed with the assumption of spherical ice particles, and retrieval results are compared globally, seasonally, and instantaneously for a case study at the rain rate level. While not at the high level of improvement shown in Tb space, the precipitation retrieval is improved as compared to one using observed Tb in correlation, bias, and root-mean-square error. Reported improvements, while modest in magnitude, advance the retrieval to more physical consistency which allows for deeper insight into ice particle properties associated with precipitation.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Diagnosis and Classification of Typhoon-Associated Low-Altitude Turbulence
           Using HKO-TDWR Radar Observations and Machine Learning
    • Authors: Jingxiao Cai;Yan Zhang;Richard J. Doviak;Yunish Shrestha;P. W. Chan;
      Pages: 3633 - 3648
      Abstract: Turbulence has been one of the major concerns for aviation safety. This paper applies evolutionary machine learning (ML) technology to turbulence level classification for civil aviation. An artificial neural network ML approach based on radar observation is developed for classifying the cubed root of the Eddy Dissipation Rate (EDR)1/3, an accepted measure of turbulence intensity. The approach is validated using Typhoon weather data collected by Hong Kong Observatory’s Terminal Doppler Weather Radar (TDWR) located near Hong Kong International Airport and comparing TDWR EDR1/3 detections and predictions with in situ EDR1/3 measured by commercial aircraft. The testing results verified that the ML approach performs reasonably well for both detecting and predicting tasks.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Reflection Symmetry Approximation of Multilook Polarimetric SAR Data and
           its Application to Freeman–Durden Decomposition
    • Authors: Wentao An;Mingsen Lin;
      Pages: 3649 - 3660
      Abstract: Freeman–Durden decomposition is a frequently used technique to analyze the scattering characteristics of multilook Polarimetric Synthetic Aperture Radar (POLSAR) data. When it is applied to the real POLSAR data, two problems emerge, namely, the volume scattering overestimation and negative powers. Many researchers think these two problems are caused by the insufficient decomposition algorithm, and several improvements are proposed. However, the improved decomposition algorithms become more and more complicated, and some new problems such as the decomposed component is not model based also emerge. In this paper, we try to solve the two problems through another way. We think they are caused not by the insufficient decomposition algorithm but by the dogmatic input. Freeman–Durden decomposition explicitly assumes reflection symmetry. Its input is a direct truncation of the measured coherency matrix. The truncation can be regarded as a reflection symmetry approximation (RSA) of the measured coherency matrix. We first show some reasons why we do not think the truncation is a good RSA. Then, a new RSA is proposed based on the sum of three reflection symmetry components derived from the measured coherency matrix. Experimental results with several real POLSAR images show that, if the new RSA is used as the input of Freeman–Durden decomposition, the above-mentioned two problems no longer exist.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • One-Port Vector Network Analyzer Characterization of Soil Dielectric
           Spectrum
    • Authors: Arkadiusz Lewandowski;Agnieszka Szypłowska;Andrzej Wilczek;Marcin Kafarski;Justyna Szerement;Wojciech Skierucha;
      Pages: 3661 - 3676
      Abstract: Accurate determination of soil complex-dielectric-permittivity spectrum is important for various applications, especially for the development of soil moisture sensors that can be used, e.g., in agriculture and for environmental monitoring. Wideband measurement of soil dielectric spectrum requires the use of large-diameter coaxial transmission-line cells connected to a vector network analyzer (VNA). We present a new method for soil dielectric-spectrum characterization in the frequency range of 0.05–3 GHz. Our methodology is based on a wideband one-port VNA measurement of a soil sample inserted into a large-diameter coaxial cell. The key part of our approach is the use of a variable load terminating the coaxial cell to extract the scattering parameters of the sample, which are then fed into a dielectric permittivity extraction algorithm. The system provides quick and repeatable measurements without the use of flexible microwave cables. Also, application of a portable one-port VNA significantly lowers the cost of the system in comparison to two-port setups. We verify our methodology based on measurements of reference materials—polytetrafluoroethylene, isopropanol, and ethanol—and then apply it to characterize the soil samples with different moisture content and salinity. Experimental results confirm the validity of our approach.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in
           VHR Images
    • Authors: Sudipan Saha;Francesca Bovolo;Lorenzo Bruzzone;
      Pages: 3677 - 3693
      Abstract: Change detection (CD) in multitemporal images is an important application of remote sensing. Recent technological evolution provided very high spatial resolution (VHR) multitemporal optical satellite images showing high spatial correlation among pixels and requiring an effective modeling of spatial context to accurately capture change information. Here, we propose a novel unsupervised context-sensitive framework—deep change vector analysis (DCVA)—for CD in multitemporal VHR images that exploit convolutional neural network (CNN) features. To have an unsupervised system, DCVA starts from a suboptimal pretrained multilayered CNN for obtaining deep features that can model spatial relationship among neighboring pixels and thus complex objects. An automatic feature selection strategy is employed layerwise to select features emphasizing both high and low prior probability change information. Selected features from multiple layers are combined into a deep feature hypervector providing a multiscale scene representation. The use of the same pretrained CNN for semantic segmentation of single images enables us to obtain coherent multitemporal deep feature hypervectors that can be compared pixelwise to obtain deep change vectors that also model spatial context information. Deep change vectors are analyzed based on their magnitude to identify changed pixels. Then, deep change vectors corresponding to identified changed pixels are binarized to obtain a compressed binary deep change vectors that preserve information about the direction (kind) of change. Changed pixels are analyzed for multiple CD based on the binary features, thus implicitly using the spatial information. Experimental results on multitemporal data sets of Worldview-2, Pleiades, and Quickbird images confirm the effectiveness of the proposed method.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Improved Retrieval of Ice and Open Water From Sequential RADARSAT-2 Images
    • Authors: Alexander S. Komarov;Mark Buehner;
      Pages: 3694 - 3702
      Abstract: In this paper, we present a new technique for automated detection of ice and open water from sequential RADARSAT-2 ScanSAR dual-polarization HH–HV images. The technique is based on combining a previously developed approach to ice and water detection applied to single synthetic aperture radar (SAR) images with the ice motion information derived from sequential SAR images. To evaluate the new approach, it was applied to 736 SAR image pairs acquired in 2013. Compared with the previous approach, the new approach produced an increase in the fraction of correctly classified water samples from 57.7% to 72.6% while the fraction of correctly classified ice samples did not change appreciably. The overall accuracy stayed at a high level exceeding 99.8%, when compared against the Canadian Ice Service Image Analysis pure ice and water samples. Verification results for different regions and months showed that the detection accuracy exceeds 99.5% for the most regions and months. The proposed approach can also assign enhanced quality to ice and water retrievals found in the reference image. The results are particularly relevant in light of the upcoming Canadian RADARSAT Constellation Mission which will significantly increase the amount and frequency of SAR observations over the Arctic region.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Sparse Blind Deconvolution of Ground Penetrating Radar Data
    • Authors: Sajad Jazayeri;Nasser Kazemi;Sarah Kruse;
      Pages: 3703 - 3712
      Abstract: We propose an effective method for sparse blind deconvolution (SBD) of ground penetrating radar data. The SBD algorithm has no constraints on the phase of the wavelet, but the initial wavelet must be carefully captured from the data. The data are considered a convolution product of an unknown source wavelet and unknown sparse reflectivity series. The algorithm developed here is an alternating minimization technique that updates the reflectivity series and the wavelet iteratively. The reflectivity update is solved as an $ell _{2}-ell _{1}$ problem with the alternating split Bregman iteration technique. The wavelet update is solved as an $ell _{2}-ell _{2}$ problem with Wiener deconvolution. The algorithm converges to a local minimum. In order to increase the likelihood so that convergence coincides with the desired local minimum, special steps are taken to provide a proper initial wavelet. Synthetic and real data examples show that both subsurface reflectivity series and wavelet (amplitude and phase) can be estimated efficiently. The SBD method presented appears robust and compares favorably to previous studies in its resistance to noise.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Two-Step Nonlinear Chirp Scaling Method for Multichannel GEO
           Spaceborne–Airborne Bistatic SAR Spectrum Reconstructing and Focusing
    • Authors: Hongyang An;Junjie Wu;Zhichao Sun;Jianyu Yang;
      Pages: 3713 - 3728
      Abstract: Due to the high-altitude illumination and the separation of the receiver and transmitter, geosynchronous (GEO) spaceborne–airborne bistatic synthetic aperture radar (BiSAR) is more flexible and accessible in remote sensing applications. In this paper, the Doppler characteristics of GEO BiSAR with a squint receiver are analyzed. It is found that the Doppler spectrum is generally aliased in GEO BiSAR regarding the low pulse repetition frequency (PRF) adopted by the GEO SAR. In order to suppress the ambiguity without adjusting the PRF of GEO SAR, the azimuth multichannel receiving technique is applied to the receiver and then the multichannel transfer function for GEO BiSAR is derived. However, the whole bandwidth of the imaging scene is much larger than that of the center point, which requires extra receiving channels to suppress the ambiguity and thereby increasing the system complexity. A two-step nonlinear chirp scaling (NLCS) method is proposed to obtain the well-focused image with reduced receiving channels. First, a preprocessing step is conducted to achieve space-variant range cell migration correction. After that, the first-step NLCS processing is applied to equalize the 2-D space-variant Doppler centroid and thereby the Doppler bandwidth is decreased, i.e., the required number of receiving channels for reconstruction is reduced. Then, the unambiguous spectrum is reconstructed based on the proposed multichannel transfer function. Finally, the second-step NLCS processing is carried out to equalize the 2-D space-variant high-order Doppler parameters and obtain the well-focused image. The simulation results validate the effectiveness of the proposed method. With the proposed two-step NLCS method, the well-focused image for GEO BiSAR is obtained and the required number of receiving channels can be decreased, which is beneficial to reducing the system complexity and hardware cost.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Analysis for the Weakly Pareto Optimum in Multiobjective-Based
           Hyperspectral Band Selection
    • Authors: Bin Pan;Zhenwei Shi;Xia Xu;
      Pages: 3729 - 3740
      Abstract: Band selection refers to finding the most representative channels from hyperspectral images. Usually, certain objective functions are designed and combined via regularization terms. A possible drawback of these methods is that they can only generate one solution in a single run with a given band number. To overcome this problem, multiobjective (MO)-based methods, which were able to simultaneously obtain a series of subsets with different band numbers, were investigated for band selection. However, because the range of band selection problem is discrete, recently proposed weighted Tchebycheff (WT)-based MO methods may suffer weakly Pareto optimal problem. In this case, the solutions for each band number will be nonunique and no optimal solution exists. Decision makers have to manually select a unique solution for each band number. In this paper, we provide a theoretical analysis about the weakly Pareto optimal problem in band selection, and quantitatively give the boundary conditions. Moreover, we further summarize the suggestions which will help users avoid the weakly Pareto optimal problem. According to these criteria, we develop a new adaptive-penalty-based boundary intersection (APBI) framework to improve the MO algorithm in hyperspectral band selection. APBI mainly includes two advantages: 1) avoiding weakly Pareto optimum and 2) reducing the sensibility of the penalty factor. The theoretical analysis is further validated by contrast experiments. The results demonstrate that the weakly Pareto optimal solutions really exist in WT methods, while APBI can overcome this problem.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • $sigma^{0}$+ +and+Sea+Surface+Height&rft.title=Geoscience+and+Remote+Sensing,+IEEE+Transactions+on&rft.issn=0196-2892&rft.date=2019&rft.volume=57&rft.spage=3741&rft.epage=3752&rft.aulast=Passaro;&rft.aufirst=Graham&rft.au=Graham+D.+Quartly;Walter+H.+F.+Smith;Marcello+Passaro;">Removing Intra-1-Hz Covariant Error to Improve Altimetric Profiles of
           $sigma^{0}$ and Sea Surface Height
    • Authors: Graham D. Quartly;Walter H. F. Smith;Marcello Passaro;
      Pages: 3741 - 3752
      Abstract: Waveform retracking is the process by which a simple mathematical model is fitted to altimeter returns. Over the ocean, the waveform location, the amplitude, and the shape can be fitted by models with 3–5 free parameters, which may, in turn, be linked to geophysical properties of the surface of interest—principally sea surface height (SSH), wave height, and normalized backscatter strength ( $sigma ^{0}$ , related to wind speed). However, random multiplicative noise, which is due to the summation of power from multiple differently orientated surfaces, produces errors in the estimation of these model parameters. Examination of the correlations among parameters estimated for each waveform leads to simple empirical corrections that reduce the waveform-to-waveform noise in geophysical estimates, resulting in smoother (and more realistic) along-track profiles of $sigma ^{0}$ and SSH. These adjustments are fundamentally dependent upon the waveform model and retracker implemented, but when applied show improved agreement between near-simultaneous measurements from different altimeter missions. The effectiveness of these empirical adjustments is documented fully for MLE-4 retracking of the Jason-3 altimeter, with a reduction in the 1-s variance of $sigma ^{0}$ by 97%. However, the ideas are applicable and beneficial for data from other altimeters, with small improvements in $sigma ^{0}$ for MLE-3 and for AltiKa at Ka-band, while reductions in range variance of ~40% are noted for most retrackers evaluated
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Cross Correlation Singularity Power Spectrum Theory and Application in
           Radar Target Detection Within Sea Clutters
    • Authors: Gang Xiong;Caiping Xi;Dongying Li;Wenxian Yu;
      Pages: 3753 - 3766
      Abstract: The cross correlation power spectrum of multiple signal sequences in the singularity domain is studied in this paper. With theoretical derivation and quantitative analysis, the cross correlation singularity power spectrum (CSPS) distribution theory is proposed. Developed from correlation function (CF), spectrum CF (SCF), singularity power spectrum (SPS), and multifractal cross correlation analysis, the CSPS can be applied for the correlation analysis of multiple fractal time series. In this paper, the CSPS is rigorously derived based on SPS and SCF, and is verified with classical multifractal time series. Furthermore, a target detection method based on the proposed CSPS method is also proposed. The proposed methodology is tested on sea clutters, both with and without target, from the Ice Multiparameter Imaging X-Band radar data set. The simulation results indicate that the target detection based on CSPS performs better than conventional multifractal spectrum methods, and can achieve almost 100% detection probability of detecting low-observable targets within sea clutters.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Degree of Polarization-Based Data Filter for Fully Polarimetric Synthetic
           Aperture Radar
    • Authors: Fang Shang;Naoto Kishi;Akira Hirose;
      Pages: 3767 - 3777
      Abstract: This paper proposes a novel data filtering algorithm for fully polarimetric synthetic aperture radar (PolSAR) based on the degree of polarization (DoP) information. First, we define the homogeneity degree and polarization independence degree using the DoP information, and propose a feature plane to characterize the target feature. Second, employing the feature plane, we categorize the targets into three types and assign specific filtering policy for each type to estimate the optimal filtering window sizes. Finally, the $T$ -matrices of fully PolSAR data are filtered using the windows with estimated optimal sizes. Compared with boxcar filter, refined Lee filter, scattering model-based filter, and improved sigma filter in processing ALOS2-PALSAR2 data, the proposed DoP-based algorithm presents the best filtering performance.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Experimental Analysis and Empirical Model of the Complex Permittivity of
           Five Organic Soils at 1.4 GHz in the Temperature Range From −30 °C to
           25 °C
    • Authors: Valery L. Mironov;Liudmila Georgievna Kosolapova;Sergey V. Fomin;Igor V. Savin;
      Pages: 3778 - 3787
      Abstract: The dielectric measurements were made for five organic soils taken from the tundra territories of Alaska, Yamal, and Taimyr, with the content of organic matter varying from 35% to 80%. The measurements were carried out in the temperature range from −30 °C to 25 °C, frequencies from 0.45 to 16 GHz and soil moisture from close to zero to the field moisture capacity. The refractive mixing model was applied to fit the measurements of the soil’s complex refractive index (CRI) as a function of soil moisture, with the values of temperature being fixed. As a result, a respective dielectric model was developed. The amounts of bound and transient water in the thawed and frozen soils were introduced as parameters of the developed model and derived as a function of temperature and content of soil organic matter. The other parameters which concern the CRIs of soil solids as well as bound, transient, and liquid soil water or ice components were derived as a function of temperature. The errors of the proposed model estimated in terms of the values of normalized root-mean-sqaure error for the real and imaginary parts of the soil complex relative permittivity appeared to be 6%–7% and 23%, respectively. The proposed dielectric model can be applied in active and passive remote sensing, in particular, for the SMOS, SMAP, and Aquarius missions after testing in ground-based experiments.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Detecting Comma-Shaped Clouds for Severe Weather Forecasting Using Shape
           and Motion
    • Authors: Xinye Zheng;Jianbo Ye;Yukun Chen;Steve Wistar;Jia Li;Jose A. Piedra Fernández;Michael A. Steinberg;James Z. Wang;
      Pages: 3788 - 3801
      Abstract: Meteorologists use shapes and movements of clouds in satellite images as indicators of several major types of severe storms. Yet, because satellite image data are in increasingly higher resolution, both spatially and temporally, meteorologists cannot fully leverage the data in their forecasts. Automatic satellite image analysis methods that can find storm-related cloud patterns are thus in demand. We propose a machine learning and pattern recognition-based approach to detect “comma-shaped” clouds in satellite images, which are specific cloud distribution patterns strongly associated with cyclone formulation. In order to detect regions with the targeted movement patterns, we use manually annotated cloud examples represented by both shape and motion-sensitive features to train the computer to analyze satellite images. Sliding windows in different scales ensure the capture of dense clouds, and we implement effective selection rules to shrink the region of interest among these sliding windows. Finally, we evaluate the method on a hold-out annotated comma-shaped cloud data set and cross match the results with recorded storm events in the severe weather database. The validated utility and accuracy of our method suggest a high potential for assisting meteorologists in weather forecasting.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • SNPP ATMS On-Orbit Geolocation Error Evaluation and Correction Algorithm
    • Authors: Jun Zhou;Hu Yang;Kent Anderson;
      Pages: 3802 - 3812
      Abstract: For the quantitative applications of the Suomi National Polar-orbiting Partnership (SNPP) Advanced Technology Microwave Sounder (ATMS), the geolocation accuracy of its sensor data records must be quantified during its on-orbit operation. In this paper, a refined coastline inflection point method is used to evaluate the on-orbit geolocation accuracy of SNPP ATMS. It is disclosed that for SNPP ATMS, the static error term with scan-angle-dependent feature is a dominant part among all the geolocation error sources. A mathematical model is then developed to convert the in-track and cross-track geolocation errors to the beam pointing Euler angles defined in the spacecraft coordinate system, which can be further used to construct the correction matrix for on-orbit geolocation process. By using the correction matrix built in this paper, the geolocation error is obviously reduced both at nadir and at the edge of the scan. The total geolocation error at nadir before/after correction is 3.8/0.8 km at K-band, 5.6/0.8 km at Ka-band, 3.3/0.4 km at V-band, and 1.5/0.1 km at W-band. The geolocation bias at the edge of the scan line before/after correction is 4.6/1.3 km at K-band, 9.4/1.8 km at Ka-band, 4.4/2.4 km at V-band, and 3.2/0.8 km at W-band. After correction, the scan-angle-dependent feature in geolocation error is also largely reduced.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Semisupervised Stacked Autoencoder With Cotraining for Hyperspectral Image
           Classification
    • Authors: Shaoguang Zhou;Zhaohui Xue;Peijun Du;
      Pages: 3813 - 3826
      Abstract: Recently, deep learning (DL) is of great interest in hyperspectral image (HSI) classification. Although many effective frameworks exist in the literature, the generally limited availability of training samples poses great challenges in applying DL to HSI classification. In this paper, we present a novel DL framework, namely, semisupervised stacked autoencoders (Semi-SAEs) with cotraining, for HSI classification. First, two SAEs are pretrained based on the hyperspectral features and the spatial features, respectively. Second, fine-tuning is alternatively conducted for the two SAEs in a semisupervised cotraining fashion, where the initial training set is enlarged by designing an effective region growing method. Finally, the classification probabilities obtained by the two SAEs are fused using a Markov random field model solved by iterated conditional modes. Experimental results based on three popular hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art DL methods.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Calibration of SMOS Soil Moisture Retrieval Algorithm: A Case of Tropical
           Site in Malaysia
    • Authors: Chuen Siang Kang;Kasturi Devi Kanniah;Yann H. Kerr;
      Pages: 3827 - 3839
      Abstract: Soil Moisture and Ocean Salinity (SMOS) mission has successfully contributed to global soil moisture products since 2009. Validation and calibration activities were conducted worldwide, yet some of the validation results do not fulfill the targeted accuracy of ±0.04 $text{m}^{3}text{m}^{-3}$ . This paper presented the site-specific calibration of the V620 retrieval algorithm with in situ data collected at selected agricultural sites in the humid tropical regions, Malaysia. This set of data has been validated where low accuracy of SMOS soil moisture products was found. To improve the SMOS soil moisture retrieval, calibration of SMOS soil moisture retrieval algorithm based on the L-band Microwave Emission and Biosphere model and SMOS Level 1C $text{T}_{mathrm {B}}$ products, considering the local parameters was conducted. The calibration proves that these site-specific parameters improve the product’s accuracy. Validation of SMOS Level 2 product with in situ data showed bias, root-mean-square error (RMSE), and unbiased RMSE (ubRMSE) ranging from 0.050 to 0.118 $text{m}^{3}text{m}^{-3}$ , 0.068 to 0.142 $text{m}^{3}text{m}^{-3}$ , and 0.069 to 0.103 $text{m}^{3}text{m}^{-3}$ , respectively. The soil moisture retrieval based on the calibrated model showed an improved bias of 0.020–0.056 $text{m}^{3}text {m}^{-3}$ and RMSE of 0.026–0.065 $text{m}^{3}text{m}^{-3}$ . The ubRMSE ranges from 0.017 to 0.034 $text{m}^{3}text{m}^{-3}$ . Recently released SMOS-IC V105 product was also validated, where small improvements were noticed when compared to the accuracy of SMOS Level 2. This paper shows the importance of local parameters in retrieving soil moisture with higher accuracy compared to the use of global generalized parameters that are used in the original SMOS soil moisture retrieval algorithm.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Cross-Domain Distance Metric Learning Framework With Limited Target
           Samples for Scene Classification of Aerial Images
    • Authors: Li Yan;Ruixi Zhu;Nan Mo;Yi Liu;
      Pages: 3840 - 3857
      Abstract: In this paper, we concentrate on the problem of cross-domain aerial scene classification. The primary assumption of the proposed cross-domain distance metric learning (CDDML) framework is that training data are adequate in the source domain but limited in the target domain. One major problem of cross-domain scene classification caused by different dates, sensor positions, lighting conditions, and sensor types is data distribution bias. To solve this problem, the CDDML framework first replaces the existing color space with the proposed hybrid color features derived from all candidate color components to decrease the spectral shift between domains. Then, hybrid color features and bag of convolution features (BOCFs) are put into a discriminating DML (DDML) method to reduce the data distribution bias in the feature space. Finally, the image-to-subcategory distance measure is proposed to decrease the effect of intraclass variability on the nearest neighbor classifier by fusing hybrid color features and BOCF in the distance space. The experiments on three aerial target images or data sets confirm that the CDDML framework can obtain better results than most of the previous methods in the case of inadequate samples. Experimental results also demonstrate that DDML, hybrid color features, and the image-to-subcategory distance measure can increase the classification performance.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Attitude Estimation for Space Targets by Exploiting the Quadratic Phase
           Coefficients of Inverse Synthetic Aperture Radar Imagery
    • Authors: Yejian Zhou;Lei Zhang;Yunhe Cao;
      Pages: 3858 - 3872
      Abstract: This paper proposes a novel approach to interpreting the satellite attitude based on inverse synthetic aperture radar (ISAR) images. In the conventional viewpoint, quadratic and higher order phase terms of ISAR imagery are regarded as negative factors causing the defocusing phenomenon. In this paper, we introduce how to apply quadratic phase coefficients to estimate target attitude from the ISAR imagery. A geometric projection model of ISAR imaging is built according to radar line of sight, and an explicit expression is also derived to connect target attitude parameters and the image defocusing property. With the accommodation of Broyden–Fletcher–Goldfarb–Shanno algorithm, spatial-variant quadratic phase coefficients together with attitude parameters are determined by an image contrast maximization. We also extend the proposed algorithm to multistatic ISAR applications, where the quadratic phase information lying in simultaneous multistatic ISAR images can be mined to enhance the performance of target attitude estimation. Experimental results illustrate the feasibility of the proposed algorithm.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Adaptive Superpixel Generation for SAR Images With Linear Feature
           Clustering and Edge Constraint
    • Authors: Deliang Xiang;Tao Tang;Sinong Quan;Dongdong Guan;Yi Su;
      Pages: 3873 - 3889
      Abstract: Due to the speckle noise and complex geometric distortions within SAR images, it is still a challenge to develop a stable method that can produce superpixels with both high boundary adherence and visual compactness with low computational costs at the same time. In this paper, we propose an adaptive superpixel generation approach with linear feature clustering and edge constraint for synthetic aperture radar (SAR) images, which consists of three stages. First, the local gradient ratio pattern of each pixel in SAR imagery is extracted as features, which was previously proposed by us for SAR target recognition and has been proven to be insensitive to speckle noise. Second, we propose to use the feature-ratio-based edge detector with Gauss-shaped window instead of the traditional rectangle-shaped window to obtain the edge strength map and final edges for SAR images. Finally, a modified normalized cut (Ncut)-based superpixel generation strategy is adopted using a distance metric that simultaneously measures both the feature similarity and space proximity. In this strategy, we approximate the similarity measure through a positive semidefinite kernel function rather than directly using the traditional eigen-based algorithm. Therefore, the objective functions of weighted local K-means and Ncuts can achieve the same optimum point by appropriately weighting each point in this feature space, which greatly reduces the computation cost. During the linear feature clustering, the coefficient of variation is used to automatically determine the tradeoff factor between the feature similarity and space proximity, which helps change the superpixel shape and size adaptively according to the image homogeneity. Furthermore, the edge information is also introduced to constrain the clustering for the sake of high boundary adherence. By bridging the local K-means clustering and Ncuts, as well as the benefits of edge constraint, our method not only produces superpixels with -ood boundary adherence but also captures the global image structure information. Experimental results with simulated and real SAR images demonstrate the effectiveness of our proposed method, which performs better than other state-of-the-art algorithms.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • On Clustering and Embedding Mixture Manifolds Using a Low Rank
           Neighborhood Approach
    • Authors: Arun M. Saranathan;Mario Parente;
      Pages: 3890 - 3903
      Abstract: Spectra from a single intimate (nonlinear) mixture can be modeled as data points drawn from a smooth manifold. Spectral data sets containing hyperspectral observations of multiple intimate mixtures with some constituent materials in common can, therefore, be modeled as data clouds, in which each point is drawn from a union of manifolds that share a boundary. Two important steps in the processing of such data are to: 1) identify the different mixture manifolds present in the data and 2) invert the nonlinear mixing function by mapping each mixture manifold into some low-dimensional Euclidean space (manifold embedding). The present state-of-the-art algorithms for joint manifold clustering and embedding perform poorly for hyperspectral data, particularly in the embedding task. We propose a novel reconstruction-based algorithm for the improved clustering and the embedding of mixture manifolds. The algorithm attempts to reconstruct each target point as an affine combination of its nearest neighbors with an additional rank penalty on the neighborhood to ensure that only the neighbors on the same manifold as the target point are used in the reconstruction. The reconstruction matrix generated by this technique is both block diagonal and neighborhood-based, leading to improved clustering and embedding. The improved performance of the algorithm against its competitors is exhibited on a variety of simulated and real mixture data sets.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Contrario Comparison of Local Descriptors for Change Detection in Very
           High Spatial Resolution Satellite Images of Urban Areas
    • Authors: Gang Liu;Yann Gousseau;Florence Tupin;
      Pages: 3904 - 3918
      Abstract: Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high-resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation, and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Second, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an a contrario framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles, and illumination conditions.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Design of New Wavelet Packets Adapted to High-Resolution SAR Images With
           an Application to Target Detection
    • Authors: Ammar Mian;Jean-Philippe Ovarlez;Abdourrahmane Mahamane Atto;Guillaume Ginolhac;
      Pages: 3919 - 3932
      Abstract: High resolution in synthetic aperture radar (SAR) leads to new physical characterizations of scatterers which are anisotropic and dispersive. These behaviors present an interesting source of diversity for target detection schemes. Unfortunately, such characteristics have been integrated and have been naturally lost in monovariate single-look SAR images. Modeling this behavior as nonstationarity, wavelet analysis has been successful in retrieving this information. However, the sharp-edge of the used wavelet functions introduces undesired high side-lobes for the strong scatterers present in the images. In this paper, a new family of parameterized wavelets, designed specifically to reduce those side lobes in the SAR image decomposition, is proposed. Target detection schemes are then explored using this spectro-angular diversity and it can be shown that in high-resolution SAR images, the non-Gaussian and robust framework leads to better results.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Novel Unsupervised Sample Collection Method for Urban Land-Cover Mapping
           Using Landsat Imagery
    • Authors: Jiayi Li;Xin Huang;Ting Hu;Xiuping Jia;Jón Atli Benediktsson;
      Pages: 3933 - 3951
      Abstract: Land-cover mapping over urban areas using Landsat imagery has attracted considerable attention in recent years as it can promptly and accurately reflect the biophysical composition status of the urban landscape and allow further applications such as urban planning and risk management. However, due to the large diversity across different urban landscapes, adequate training sample collection for urban area mapping is both challenging and time-consuming. In this paper, we propose a novel unsupervised sample collection method for mapping urban areas using Landsat imagery. Specifically, the idea is to select reliable, representative, and diverse training samples from the images in a two-stage and iterative manner, based on a set of spectral indices (vegetation, impervious surface, soil, water). To validate the effectiveness and robustness of the proposed method, a synthetic data set was designed and a series of Landsat images over 39 representative cities from different biomes across the world was employed. The effectiveness of the proposed algorithm was quantitatively validated by assessing the quality of the automatically collected samples and the accuracy of the mapping results. In terms of the mapping performance, the proposed automatic approach can achieve a comparable mapping accuracy to supervised classification with manually collected samples. On the basis of the freely accessed Landsat data, the proposed approach demonstrates a promising potential for automatic large-scale (i.e., global) mapping over urban areas.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Cross-Domain Collaborative Learning via Cluster Canonical Correlation
           Analysis and Random Walker for Hyperspectral Image Classification
    • Authors: Yao Qin;Lorenzo Bruzzone;Biao Li;Yuanxin Ye;
      Pages: 3952 - 3966
      Abstract: This paper introduces a novel heterogeneous domain adaptation (HDA) method for hyperspectral image (HSI) classification with a limited amount of labeled samples in both domains. The method is achieved in the way of cross-domain collaborative learning (CDCL), which is addressed via cluster canonical correlation analysis (C-CCA) and random walker (RW) algorithms. To be specific, the proposed CDCL method is an iterative process of three main components, i.e., RW-based pseudolabeling, cross-domain learning via C-CCA, and final classification based on extended RW (ERW) algorithm. First, given the initially labeled target samples as the training set (TS), the RW-based pseudolabeling is employed to update TS and extract target clusters (TCs) by fusing the segmentation results obtained by RW and ERW classifiers. Second, cross-domain learning via C-CCA is applied using labeled source samples and TCs. The unlabeled target samples are then classified with the estimated probability maps using the model trained in the projected correlation subspace. The newly estimated probability map and TS are used for updating TS again via RW-based pseudolabeling. Finally, when the iterative process converges, the result obtained by the ERW classifier using the final TS and estimated probability maps is regarded as the final classification map. Experimental results on four real HSIs demonstrate that the proposed method can achieve better performance compared with the state-of-the-art HDA and ERW methods.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Efficient Detection of Buried Plastic Pipes by Combining GPR and Electric
           Field Methods
    • Authors: Xiren Zhou;Huanhuan Chen;Tong Hao;
      Pages: 3967 - 3979
      Abstract: In this paper, an efficient plastic pipe detecting model is proposed, which combines the ground penetrating radar (GPR) and the electric field method. The model consists of the electric field locating model (EFLM) and the GPR B-scan image interpreting (GBII) model. Synchronized electric field and GPR data are collected through a data acquisition device dedicatedly designed for the swift and accurate estimation of buried plastic pipes. The EFLM estimates the approximate locations of underground plastic pipes from the electric field data quickly, separates a GPR B-scan image into segments, keeps the segments that might contain hyperbolas, and discards the irrelevant ones. Then, the GBII model interprets the depth and radius of the buried pipe in the kept segments. Our numerical simulations and experiments prove that by utilizing the EFLM, the 1-D electric field data could be processed quickly and the GPR B-scan image could be segmented with part of irrelevant pixels discarded, while hyperbolas in the kept image segments could be automatically and accurately fitted. With our proposed model, the depth and radius of the buried pipes could be efficiently obtained.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Hyperspectral Unmixing With Spectral Variability Using Adaptive Bundles
           and Double Sparsity
    • Authors: Tatsumi Uezato;Mathieu Fauvel;Nicolas Dobigeon;
      Pages: 3980 - 3992
      Abstract: Spectral variability is one of the major issues when conducting hyperspectral unmixing. Within a given image composed of some elementary materials (herein referred to as endmember classes), the spectral signatures characterizing these classes may spatially vary due to intrinsic component fluctuations or external factors (illumination). These redundant multiple endmember spectra within each class adversely affect the performance of unmixing methods. This paper proposes a mixing model that explicitly incorporates a hierarchical structure of redundant multiple spectra representing each class. The proposed method is designed to promote sparsity on the selection of both spectra and classes within each pixel. The resulting unmixing algorithm is able to adaptively recover several bundles of endmember spectra associated with each class and robustly estimate abundances. In addition, its flexibility allows a variable number of classes to be present within each pixel of the hyperspectral image to be unmixed. The proposed method is compared with other state-of-the-art unmixing methods that incorporate sparsity using both simulated and real hyperspectral data. The results show that the proposed method can successfully determine the variable number of classes present within each class and estimate the corresponding class abundances.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Empirical Methods for Remote Sensing of Nitrogen in Drylands May Lead to
           Unreliable Interpretation of Ecosystem Function
    • Authors: Hamid Dashti;Nancy F. Glenn;Susan Ustin;Jessica J. Mitchell;Yi Qi;Nayani T. Ilangakoon;Alejandro N. Flores;José Luis Silván-Cárdenas;Kaiguang Zhao;Lucas P. Spaete;Marie-Anne de Graaff;
      Pages: 3993 - 4004
      Abstract: Nitrogen (N) has been linked to different ecosystem processes, and retrieving this important foliar biochemical constituent from remote sensing observations is of widespread interest. Since N is not explicitly represented in physically based radiative transfer models, empirical methods have been used as an alternative. The spectral bands selected during the calibration of empirical methods have been interpreted in the context of light-N interactions and, consequently, ecosystem function. The low amount of leaves on shrubs and their sparse distribution in drylands create an environment, in which the canopy structure and the bright background soil play an important role in the canopy total radiation budget. In this paper, we examine the assumption that removing the impact of canopy structure and soil will result in improved N retrieval using the empirical methods. We report the inconsistencies in the selection of spectral bands among the empirical approaches. Moreover, these methods are sensitive to the scale of the study and band transformations. Using the generalized theory of canopy spectral invariants, we found that at the canopy scale, a combination of canopy structure and soil dominates the total canopy radiation. At the plot scale, soil contributes up to 95% of the total reflectance. Correction for these two confounding factors leads to no correlation between N and vegetation reflectance at both scales. We conclude that while cross-validated predictive models may be statistically achievable, caution should be taken when interpreting their results in the context of N-light interactions and ecosystem function. Our approach can be extended to all terrestrial ecosystems with multiple layers of canopy and understory.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Semiautomatic Construction of 2-D Façade Footprints From Mobile
           LiDAR Data
    • Authors: Shaobo Xia;Ruisheng Wang;
      Pages: 4005 - 4020
      Abstract: Although mobile light detection and ranging (LiDAR) technology has excellent potential in mapping street scenes, there is little research in constructing façade footprints from unorganized, uneven, and incomplete mobile LiDAR point clouds. In fact, façade footprint vectorization from mobile LiDAR data still involves a lot of manual work, especially in complex street scenes with various types of buildings. In this paper, we present a new and effective framework for extracting 2-D façade footprints from mobile LiDAR point clouds. The proposed framework consists of three steps: 1) line segment extraction from projected point clouds based on a hypotheses and selection strategy; 2) completion of missing parts between adjacent walls using line intersections; and 3) delineation of footprints through finding the least cost path in the graph of the line segments. We compare our method with several existing ones and discuss its robustness against data missing and noise such as nonwall structures and vegetation. Our proposed method is also tested in two large-scale data sets, a residential data set, and an urban data set. The coverage ratio, i.e., the percentage of outer wall points covered by the generated outlines in the residential data set is 93.4% and 91.7% in the urban data set is achieved. The mean distance between points of ground truth and constructed footprints for the residential data set and urban data set is 0.019 and 0.028 m, respectively. The experimental results demonstrate that the proposed framework is effective in modeling various façade footprints from mobile LiDAR point clouds.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Learning Deep Ship Detector in SAR Images From Scratch
    • Authors: Zhipeng Deng;Hao Sun;Shilin Zhou;Juanping Zhao;
      Pages: 4021 - 4039
      Abstract: Recently, deep learning-based methods have brought new ideas for ship detection in synthetic aperture radar (SAR) images. However, several challenges still exist: 1) deep models contain millions of parameters, whereas the available annotated samples are not sufficient in number for training. Therefore, most deep detectors have to fine-tune networks pre-trained on ImageNet, which incurs learning bias due to the huge domain mismatch between SAR images and ImageNet images. Furthermore, it has a little flexibility to redesign the network structure; and 2) ships in SAR images are relatively small in size and densely clustered, whereas most deep detectors have poor performance with small objects due to the rough feature map used for detection and the extreme foreground–background imbalance. To address these problems, this paper proposes an effective approach to learn deep ship detector from scratch. First, we design a condensed backbone network, which consists of several dense blocks. Hence, earlier layers can receive additional supervision from the objective function through the dense connections, which makes it easy to train. In addition, feature reuse strategy is adopted to make it highly parameter efficient. Therefore, the backbone network could be freely designed and effectively trained from scratch without using a large amount of annotated samples. Second, we improve the cross-entropy loss to address the foreground–background imbalance and predict multi-scale ship proposals from several intermediate layers to improve the recall rate. Then, position-sensitive score maps are adopted to encode position information into each ship proposal for discrimination. The comparison results on the Sentinel-1 data set show that: 1) learning ship detector from scratch achieved better performance than ImageNet pre-trained model-based detectors and 2) our method is more effective than existing algorithms for detecting the small and d-nsely clustered ships.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Textural Noise Correction for Sentinel-1 TOPSAR Cross-Polarization Channel
           Images
    • Authors: Jeong-Won Park;Joong-Sun Won;Anton A. Korosov;Mohamed Babiker;Nuno Miranda;
      Pages: 4040 - 4049
      Abstract: The thermal noise-induced distortions in a Sentinel-1 terrain observation with progressive scans synthetic aperture radar image cannot be corrected by the noise equivalent sigma nought (NESZ) subtraction only. Since the thermal noise is scaled during synthetic aperture radar processing, it resides not only as an additive noise in each pixel but also as a multiplicative noise in the interpixel contrast. In this paper, we investigate the noise characteristics and propose an efficient method for the multiplicative textural noise correction. The core ideas are to find the optimal coefficient of the noise-induced standard deviation (SD) and model the noise contribution to the local SD as a function of the NESZ and the signal-to-noise ratio. Denoising is accomplished by a subwindow-wise adaptive rescaling of the pixel values. The improvements in the first- and second-order statistical textural features demonstrate the effectiveness of the proposed method.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Comparative Evaluation of Sea Ice Lead Detection Based on SAR Imagery and
           Altimeter Data
    • Authors: Nicolas Longépé;Pierre Thibaut;Rodolphe Vadaine;Jean-Christophe Poisson;Amandine Guillot;Francois Boy;Nicolas Picot;Franck Borde;
      Pages: 4050 - 4061
      Abstract: The detection of sea ice leads is a prerequisite for the estimation of ice freeboard and thickness from altimeter data. The classification of altimeter waveforms is generally performed using statistical parameters on the echo power or machine learning approaches directly on the waveforms. The validation and optimization of such algorithms can be carried out using a set of reference cases provided by Earth Observation images. In this paper, we first developed a new lead detector based on Sentinel-1 (S-1) synthetic aperture radar (SAR) images. A robust and consistent methodology for the joint assessment of Altimeter and SAR leads detector is then provided. We propose to fully account for the 2-D geometric problem when comparing the 1-D altimeter track and 2-D SAR image. The surface of the lead intersecting the altimeter footprint and its distance to nadir are considered here. Based on collocated Sentinel-3 (S-3) altimeter data and S-1 images, the performance of our S-3 lead detector is fully assessed. A new parameterization is found resulting in a better tradeoff between good detection and false alarm rate. A similar analysis is performed using AltiKa altimeter data, showing enhanced performance for S-3 altimeter data acquired in Delay-Doppler mode with reduced off-nadir returns.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Cloud Detection in Remote Sensing Images Based on Multiscale
           Features-Convolutional Neural Network
    • Authors: Zhenfeng Shao;Yin Pan;Chunyuan Diao;Jiajun Cai;
      Pages: 4062 - 4076
      Abstract: Cloud detection in remote sensing images is a challenging but significant task. Due to the variety and complexity of underlying surfaces, most of the current cloud detection methods have difficulty in detecting thin cloud regions. In fact, it is quite meaningful to distinguish thin clouds from thick clouds, especially in cloud removal and target detection tasks. Therefore, we propose a method based on multiscale features-convolutional neural network (MF-CNN) to detect thin cloud, thick cloud, and noncloud pixels of remote sensing images simultaneously. Landsat 8 satellite imagery with various levels of cloud coverage is used to demonstrate the effectiveness of our proposed MF-CNN model. We first stack visible, near-infrared, short-wave, cirrus, and thermal infrared bands of Landsat 8 imagery to obtain the combined spectral information. The MF-CNN model is then used to learn the multiscale global features of input images. The high-level semantic information obtained in the process of feature learning is integrated with low-level spatial information to classify the imagery into thick, thin and noncloud regions. The performance of our proposed model is compared to that of various commonly used cloud detection methods in both qualitative and quantitative aspects. Compared to other cloud detection methods, the experimental results show that our proposed method has a better performance not only in thick and thin clouds but also in the entire cloud regions.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Destriping Algorithms Based on Statistics and Spatial Filtering for
           Visible-to-Thermal Infrared Pushbroom Hyperspectral Imagery
    • Authors: Jianxin Jia;Yueming Wang;Xiaoyu Cheng;Liyin Yuan;Ding Zhao;Qi Ye;Xiaoqiong Zhuang;Rong Shu;Jianyu Wang;
      Pages: 4077 - 4091
      Abstract: Following calibration, hyperspectral images remain affected by spatial dimension nonuniformity, i.e., stripe noise, due to stray light interference, slit contamination, and instrument instability. The full spectrum airborne hyperspectral imager (FAHI) is a Chinese next-generation pushbroom sensor with a spectral range covering the visible near-infrared, shortwave-infrared (SWIR), and thermal infrared regions with spectral sampling intervals of 2.34, 3, and 32 nm, respectively. However, the residual stripe noise remains in FAHI images after relative radiometric correction based on the laboratory calibration, especially in low signal-to-noise ratio bands. To solve this problem, a new technique combining image statistics and spatial filtering algorithms has been developed for FAHI image correction. In this method, image statistics are obtained to calculate the gain and offset of each pixel for image nonuniformity correction. Then, a spatial filter removes the residual stripes. This paper presents the principles of this method along with details of validation experiments and results. To validate the effectiveness of the proposed method, comparison with two destriping methods and quantitative analyses are carried out. Moreover, the method is applied to an SWIR hyperspectral image from the TianGong-1 spacecraft, yielding good results. The experimental results suggest that the proposed method is convenient and practical for improving the relative radiometric accuracy of airborne/spaceborne hyperspectral images.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Spectral Responses of Heavy Metal Contaminated Soils in the Vicinity of a
           Hydrothermal Ore Deposit: A Case Study of Boksu Mine, South Korea
    • Authors: Ji Hye Shin;Jaehyung Yu;Lei Wang;Jieun Kim;Sang-Mo Koh;Soon-Oh Kim;
      Pages: 4092 - 4106
      Abstract: This paper investigated the spectral characteristics of heavy metal contaminated soils of a hydrothermal ore deposit developed in carbonate host rock associated with heavy metal concentration and mineral composition. The results showed that spectral response of heavy metal contaminated soils was statistically correlated with zinc, cadmium, and lead concentrations. Empirical equations for predicting zinc, cadmium, and lead concentrations were derived. Spectral characteristics of the soils were expressed by smectite, chlorite, tremolite, and talc which resulted from hydrothermal alteration and weathering products of the parent rocks. The stepwise multiple linear regression (SMLR) model of zinc, cadmium, and lead was statistically satisfactory with $R^{2}$ greater than 0.7. The SMLR results indicated that the spectral response to cadmium and zinc concentration was sensitive to reflectance at 1850 nm and first derivative at ~950 and 2154 nm corresponding to the smectite absorption features. On the other hand, lead concentration is closely related to first derivatives at 1453, 2316, and 2337 nm, which are absorption features of chlorite, tremolite, and talc. These results revealed that the spectral bands sensitive to the heavy metal concentration varied with the geochemical absorption mechanism between specific minerals and heavy metal elements. Therefore, the geological setting of the soils is one of the major controlling factors associated with spectral response to heavy metal contamination. Given the fact that a hydrothermal ore deposit is one of the most widely distributed types, the laboratory result of this paper may be applied to the real-world cases with similar geological environments.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A New Motion Parameter Estimation and Relocation Scheme for Airborne
           Three-Channel CSSAR-GMTI Systems
    • Authors: Yongkang Li;Yongliang Wang;Baochang Liu;Shuangxi Zhang;Laisen Nie;Guoan Bi;
      Pages: 4107 - 4120
      Abstract: This paper proposes a new scheme of motion parameter estimation and relocation for airborne three-channel circular stripmap synthetic aperture radar (CSSAR)-ground moving target indication (GMTI) systems. Compared with the conventional straight-path SAR, the parameter estimation of a target is more challenging because the target’s range history and signal model are more complicated due to the complexity of the relative motion between CSSAR and ground moving target. In this paper, the signal model of a ground moving target and the expression for its along-track interferometric (ATI) phase from the environment of airborne three-channel CSSAR are derived. The coupling effect among the target’s motion and position parameters is also figured out. Then, a scheme of motion parameter estimation and relocation is proposed. The proposed scheme utilizes the ATI phase and the quadratic-term coefficient in the range equation to estimate the target’s motion and position parameters and utilizes an iterative strategy to address the coupling effect among these parameters. Numerical simulations are conducted to validate the satisfactory performance achieved by the proposed algorithm.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • A Novel Framework for 2.5-D Building Contouring From Large-Scale
           Residential Scenes
    • Authors: Jianli Du;Dong Chen;Ruisheng Wang;Jiju Peethambaran;P. Takis Mathiopoulos;Lei Xie;Ting Yun;
      Pages: 4121 - 4145
      Abstract: This paper introduces a novel methodology for residential building contouring from large-scale airborne point clouds. Unlike other methods that handle linearization and regularization of the linear primitives separately by imposing rigid constraints, we propose an optimization-based linearization and global regularization to form accurate, topologically error-free, and lightweight polygons. To this end, we enhance the classic density-based spatial clustering of applications with noise algorithm to segment individual building entities at the instance level. The initial contours of each individual building are then delineated and further decomposed by a novel topologically aware propagation process and a global optimization technique. The decomposed linear primitives are fed into the global regularization step, from which the regular shapes are learned and enforced hierarchically by imposing constraints, such as parallelism, homogeneity, orthogonality, and collinearity. Based on the concept of hybrid representation, the regularized and unaltered linear primitives are jointly connected in an esthetic way. Various experiments using representative buildings and large-scale residential scenes from the Dutch AHN3 data set have shown that the proposed methodology generates meaningful building contouring representation in terms of accuracy, compactness, topology, and levels of detail abstraction while being robust and scalable.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • IEEE Access
    • Pages: 4147 - 4147
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
  • Introducing IEEE Collabratec
    • Pages: 4148 - 4148
      Abstract: Advertisement, IEEE.
      PubDate: June 2019
      Issue No: Vol. 57, No. 6 (2019)
       
 
 
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