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  Subjects -> ELECTRONICS (Total: 179 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 78)
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: 315)
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: 269)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 105)
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: 92)
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: 191)
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: 66)
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: 19)
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: 71)
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: 24)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 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: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 12)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 24)
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  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 168)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 28)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
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: 18)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 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)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 53)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 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: 191  
  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: April 2019
      Issue No: Vol. 57, No. 4 (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: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • IEEE Transactions on Geoscience and Remote Sensing institutional listings
    • Abstract: Presents the GRSS society institutional listings.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Deep-Learning Schemes for Full-Wave Nonlinear Inverse Scattering Problems
    • Authors: Zhun Wei;Xudong Chen;
      Pages: 1849 - 1860
      Abstract: This paper is devoted to solving a full-wave inverse scattering problem (ISP), which is aimed at retrieving permittivities of dielectric scatterers from the knowledge of measured scattering data. ISPs are highly nonlinear due to multiple scattering, and iterative algorithms with regularizations are often used to solve such problems. However, they are associated with heavy computational cost, and consequently, they are often time-consuming. This paper proposes the convolutional neural network (CNN) technique to solve full-wave ISPs. We introduce and compare three training schemes based on U-Net CNN, including direct inversion, backpropagation, and dominant current schemes (DCS). Several representative tests are carried out, including both synthetic and experimental data, to evaluate the performances of the proposed methods. It is demonstrated that the proposed DCS outperforms the other two schemes in terms of accuracy and is able to solve typical ISPs quickly within 1 s. The proposed deep-learning inversion scheme is promising in providing quantitative images in real time.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Azimuth Signal Multichannel Reconstruction and Channel Configuration
           Design for Geosynchronous Spaceborne–Airborne Bistatic SAR
    • Authors: Junjie Wu;Zhichao Sun;Hongyang An;Jingyi Qu;Jianyu Yang;
      Pages: 1861 - 1872
      Abstract: In geosynchronous spaceborne-airborne bistatic synthetic aperture radar (GEO-BiSAR) system, the airborne platform achieves high-resolution imaging by passively receiving the signal from the interested scenario. In this paper, the Doppler characteristics of GEO-BiSAR and the individual contribution of the transmitter and the receiver are first analyzed. The airborne receiver is found to be the dominant contributor for the total Doppler bandwidth, which will lead to Doppler spectrum aliasing regarding the low pulse repetition frequency (PRF) adopted by the GEO-SAR. In order to suppress the Doppler ambiguity without adjusting the PRF of GEO-SAR, azimuth multichannel receiving technique is introduced to the airborne receiver. The multichannel transfer function is derived based on the method of series reversion and the spectrum reconstruction algorithm is then modified for multichannel GEO-BiSAR. Moreover, the reconstruction performance is closely related to the corresponding spacing between each channel (i.e., channel configuration). Therefore, the channel configuration design for GEO-BiSAR aims at optimizing the azimuth ambiguity-to-signal ratio with a satisfactory level of signal-to-noise ratio scaling factor by adjusting the channel configuration. The channel configuration design is modeled as a constrained single objective optimization problem (CSOP). Then, a channel configuration design method based on differential evolution and feasibility rule is proposed to solve the CSOP and obtain the channel configuration for the receiver with the optimal reconstruction performance. Finally, simulations results are presented to verify the effectiveness of the proposed method, and characteristics of channel configuration are analyzed in detail, which can be a practical guide for the implementation of multichannel GEO-BiSAR systems.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • 3-D Reconstruction Method for Complex Pore Structures of Rocks Using a
           Small Number of 2-D X-Ray Computed Tomography Images
    • Authors: Yang Ju;Yaohui Huang;Wenbo Gong;Jiangtao Zheng;Heping Xie;Li Wang;Xu Qian;
      Pages: 1873 - 1882
      Abstract: Underground hydrocarbon reservoir rocks comprise numerous multiscale irregular pores that significantly affect the mechanical and fluid transport properties of the rock. It is considerably challenging for in situ geological monitoring and laboratory tests to accurately characterize the changes in the interior structure and the corresponding mechanical properties of the rock mass during dynamic excavation processes. The 3-D numerical reconstruction models that are based on the statistical information extracted from X-ray computed tomography (XCT) images provide a feasible method to obtain and characterize the interior pore structures and their effects on the physical responses of reservoir rocks. However, obtaining sufficient high-resolution 2-D XCT images is economically expensive by the traditional fan beam CT scan system. Reconstructing 3-D porous structures by computational methods using statistical information extracted from XCT images usually has low efficiency. Therefore, in this paper, we introduce a novel method to numerically reconstruct natural sandstone rock using a small number of 2-D XCT images. The Bayesian information criterion was used to determine the minimum number of 2-D XCT images required to ensure the expected reconstruction accuracy. A multithread parallel reconstruction scheme was employed to improve the efficiency. The accuracy of the proposed method was verified by comparing the statistical correlation functions, geometrical and topological characteristics, and mechanical properties of pore structures between the reconstructed model and a sandstone prototype. This paper provides a method to achieve fast, economic, and accurate 3-D reconstruction of porous rock.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Structured Graph Dictionary Learning and Application on the Seismic
    • Authors: Lina Liu;Jianwei Ma;
      Pages: 1883 - 1893
      Abstract: Sparse coding method has been used for seismic denoising, as the data can be sparsely represented by the sparse transform and dictionary learning (DL) methods. DL methods have attracted wide attention because the learned dictionary is adaptive. However, for seismic denoising, the dictionary learned from the noise data is a mix of atoms representing seismic data patterns and atoms representing noise patterns. To make the dictionary contain more atoms to represent seismic data, we consider adding to the dictionary the local and nonlocal similarities of the data via the structured graph and propose a new DL method, namely, the structured graph dictionary learning (SGDL). The atoms of dictionary learned by the SGDL are smooth, which implies smoothness of any signal represented over this dictionary. In addition, in the dictionary domain, we use the nonlocal model, namely, SSC-GSM that connects Gaussian scale mixture (GSM) with simultaneous sparse coding (SSC), to represent the seismic data. We apply the method to the synthetic data and two kinds of field data. Results show that our method can better remove strong noise and retain the seismic weak events also.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Unsupervised Hierarchical Land Classification Using Self-Organizing
           Feature Codebook for Decimeter-Resolution PolSAR
    • Authors: Hyunsoo Kim;Akira Hirose;
      Pages: 1894 - 1905
      Abstract: In this paper, we propose a hierarchical polarization feature generation using a self-organizing codebook to realize unsupervised land classification that fully utilizes the detailed polarization information contained in high-resolution polarimetric synthetic aperture radar (PolSAR) data. PolSAR has reached a decimeter-level high resolution. In general, conventional methods lower the resolution of the PolSAR data to 10-20 m in the real-space distance to classify observation regions into land classes such as farm, forest, and town. However, lowering resolution prevents us from discovering new land classes potentially enabled by the resolution enhancement. The hierarchical method we propose here not only classifies observation regions successfully into land classes such as farm, forest, and town that humans can naturally distinguish but also discovers new land subclasses findable only in high-resolution PolSAR data. We explain these two types of our achievements (classification/discovery) through experimental results for Japan Aerospace Exploration Agency's polarimetric and interferometric airborne SAR-L2 data having decimeter resolution.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Micro-Doppler Ambiguity Resolution With Variable Shrinkage Ratio Based on
           Time-Delayed Cross Correlation Processing for Wideband Radar
    • Authors: Xiangyu Xiong;Hui Liu;Zhenmiao Deng;Maozhong Fu;Wei Qi;Yunjian Zhang;
      Pages: 1906 - 1917
      Abstract: Vibration or rotation on a target or any structure of a target might induce a frequency modulation on the returned radar signal, which is called micro-Doppler effect, and is widely used for many of the remote sensing applications such as target detection, classification, and recognition. However, the micro-Doppler frequency could be large and it may cause micro-Doppler ambiguities which are undesirable in these applications. In this paper, we propose a novel and flexible method to resolve the micro-Doppler ambiguities based on the time-delayed cross correlation processing of wideband radar echoes for range-spread targets. Variable-sized shrinkage ratio can be obtained to reduce the micro-Doppler frequency of the signal to an appropriate numerical value, and the micro-Doppler ambiguities can be resolved in different situations. The effectiveness of the proposed method is validated by simulations and real data experiments.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Nonlinear Guided Filter for Polarimetric SAR Image Despeckling
    • Authors: Xiaoshuang Ma;Penghai Wu;Huanfeng Shen;
      Pages: 1918 - 1927
      Abstract: Despeckling is a fundamental preprocessing step for applications using polarimetric synthetic aperture radar data in most cases. In this paper, a guided filter with nonlinear weight kernels and adaptive filtering windows is presented for PolSAR image despeckling, in which the guidance image is constructed by a weighted average using the statistical traits of the speckled image. The output result is then estimated by another weighted average, with the aid of the fully polarimetric information from both the guidance image and the speckled image. In the experimental part, the filtering results obtained with both simulated and real PolSAR images reveal the positive performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art algorithms. Furthermore, the relatively low computational complexity is another strength of the proposed method.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Gauge-Adjusted Global Satellite Mapping of Precipitation
    • Authors: Tomoaki Mega;Tomoo Ushio;Matsuda Takahiro;Takuji Kubota;Misako Kachi;Riko Oki;
      Pages: 1928 - 1935
      Abstract: A rain-gauge-adjusted algorithm for global satellite mapping of precipitation (GSMaP) that estimates the surface precipitation rate with resolutions of 0.1° and 1 h over the globe is described herein. Precipitation is one of the most important parameters of the earth's system, and its global distribution and changes are essential data for modeling the water cycle, maintaining ecosystems, increasing agricultural production, improving weather forecasting precision, and implementing flood warning systems. In the Global Precipitation Measurement project, integrated products of high-resolution mapping of precipitation, obtained from microwave measurements made by a constellation satellite and infrared radiometers in geostationary orbit, are developed and supplied to the public. However, these high-resolution products, such as GSMaP_MVK, sometimes underestimate surface precipitation, introducing large errors into hydrological modeling. This paper combines the global gauge data set with GSMaP_MVK, using a new algorithm [gauge-adjusted GSMaP (GSMaP_Gauge)], described and evaluated herein using local radar and rain-gauge data sets. This algorithm outperforms other GSMaP products in all validation tests.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Detection and Classification of Continuous Volcano-Seismic Signals With
           Recurrent Neural Networks
    • Authors: Manuel Titos;Angel Bueno;Luz García;M. Carmen Benítez;Jesús Ibañez;
      Pages: 1936 - 1948
      Abstract: This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995-2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016-2017 by the Spanish Antarctic scientific campaign. Despite the variations in the geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, even if seismic sources change over time.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Background-Free Ground Moving Target Imaging for Multi-PRF Airborne SAR
    • Authors: Guanghu Jin;Zhen Dong;Feng He;Anxi Yu;
      Pages: 1949 - 1962
      Abstract: Synthetic aperture radar (SAR) is an advanced imaging sensor that can image side-looking terrain. Until now, SAR schemes and imaging theories have been researched for stationary scenes. Unlike a stationary scene, due to unknown motion parameters, a ground moving target (GMT) is shifted and defocused in a background image, which causes difficulties in their use for further applications. This paper proposes a multiple pulse repetition frequency (PRF) airborne SAR scheme for background-free GMT imaging. In the proposed scheme, pulses are transmitted by a nonuniform pulse repetition time, and echoes are divided into groups with different PRFs. For spectra with different PRFs, due to their diverse respective spectra extending periods, periodical extending GMT spectra will not appear at the same location except for the original spectra. A theorem is proposed to prove that the spectra can be extracted intact, undistorted, and unpolluted. In light of the divergence of the GMT spectra location from that of the clutter, filter banks are designed to locate, extract, and reconstruct the GMT spectra out of the mixed spectra that contain the GMT and clutter. For GMT SAR imaging, a moving target is regarded as a steady target under an equivalent geometry with a new equivalent squint angle and a new SAR platform velocity. The SAR image is obtained with the range-Doppler algorithm with equivalent geometry parameters that are estimated from the Doppler parameters. Experimental results obtained from using extensive numerical simulated data validate our proposed approach.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Modeling Multifrequency Pol-InSAR Data From the Percolation Zone of the
           Greenland Ice Sheet
    • Authors: Georg Fischer;Konstantinos P. Papathanassiou;Irena Hajnsek;
      Pages: 1963 - 1976
      Abstract: The analysis of data from an airborne synthetic aperture radar (SAR) campaign in the percolation zone of Greenland revealed an interferometric coherence undulation behavior with respect to vertical wavenumber, which cannot be explained with existing models. We propose a model extension that accounts for scattering from distinct layers below the surface. Simulations show that the periodicity of the coherence undulation is mainly driven by the vertical distance between dominant subsurface layers, while the amplitude of the undulation is determined by the ratio between scattering from distinct layers and scattering from the firn volume. We use the model to interpret quad-pol SAR data at X-, C-, S-, L-, and P-bands. The inferred layer depths match layer detections in ground-based radar data and in situ measurements. We conclude that in the percolation zone, scattering from subsurface layers has to be taken into account to correctly interpret SAR data and demonstrate the potential to retrieve geophysical information about the vertical subsurface structure.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Improving the Calibration of Suomi NPP VIIRS Thermal Emissive Bands During
           Blackbody Warm-Up/Cool-Down
    • Authors: Wenhui Wang;Changyong Cao;Alexander Ignatov;Xingming Liang;Zhenglong Li;Likun Wang;Bin Zhang;Slawomir Blonski;Jun Li;
      Pages: 1977 - 1994
      Abstract: The Suomi National Polar-orbiting Partnership Program Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive bands (TEB) have been performing well during nominal operations since launch. However, small but persistent calibration anomalies are observed in all TEBs during the quarterly blackbody (BB) warm-up/cool-down (WUCD) events. As a result, the time series of daytime sea surface temperature (SST) (derived from bands M15-M16) show warm spikes on the order of 0.25 K. This paper suggests that VIIRS TEB WUCD biases are band dependent, with daily-averaged biases about -0.04 and 0.05 K for I4 and I5, and -0.05, -0.05, 0.11, 0.09, and 0.05 K for M12-M16, respectively. Two correction methods-Ltrace and WUCD-C-have been implemented and evaluated using colocated observations from the Cross-track Infrared Sounder (CrIS), radiative transfer simulations, and SST retrievals. Also an error in the National Oceanic and Atmospheric Administration operational processing was identified and fixed. Both correction methods effectively minimize WUCD-induced SST anomalies. The Ltrace method works well for I5, M12, and M14-M16, with residual biases about 0.01 K. The WUCD-C method, on the other hand, performs well to correct WUCD biases in all TEBs, with residual biases also about 0.01 K. However, it introduces warm biases relative to CrIS at cold scene temperatures, which requires further study. Applying nonequal BB thermistor weights improves calibration at BB temperature set points, but its impact on daily-averaged WUCD biases is small. The proposed methodologies may also be applied to the VIIRS onboard the follow-on Joint Polar Satellite System satellites.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Illumination-Robust Subpixel Fourier-Based Image Correlation Methods Based
           on Phase Congruency
    • Authors: Zhen Ye;Xiaohua Tong;Shouzhu Zheng;Chengcheng Guo;Sa Gao;Shijie Liu;Xiong Xu;Yanmin Jin;Huan Xie;Sicong Liu;Peng Chen;
      Pages: 1995 - 2008
      Abstract: The Fourier-based image correlation technique has been widely concerned due to its accuracy, efficiency, and robustness to image contrast and brightness. Accordingly, a variety of subpixel methods have been proposed. However, the detailed subpixel-level influence of the complicated radiometric variations has yet to be investigated, and few corresponding improvements have been made. This paper presents a novel illumination-robust subpixel Fourier-based image correlation method based on phase congruency. Both the magnitude and orientation information of the phase congruency features are adopted to construct a structural image representation. The image representation is then embedded into the correlation scheme of the subpixel methods, either by linear phase estimation in the frequency domain or by kernel fitting in the spatial domain, achieving two improved subpixel methods. The proposed methods integrate the advantages of the structural image representation and the original correlation scheme, and make full use of both global and local phase information to achieve illumination-robust correlation. Experiments undertaken with both simulated and real radiometric differences were carried out with ground-truth subpixel shifts. The performances of the proposed methods and the other state-of-the-art subpixel Fourier-based correlation methods were evaluated and compared. The experimental results indicate that the proposed methods outperform the other methods in the presence of diverse radiometric variations, in both accuracy and robustness.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Partially Asynchronous Distributed Unmixing of Hyperspectral Images
    • Authors: Pierre-Antoine Thouvenin;Nicolas Dobigeon;Jean-Yves Tourneret;
      Pages: 2009 - 2021
      Abstract: So far, the problem of unmixing large or multitemporal hyperspectral data sets has been specifically addressed in the remote sensing literature only by a few dedicated strategies. Among them, some attempts have been made within a distributed estimation framework, in particular, relying on the alternating direction method of multipliers. In this paper, we propose to study the interest of a partially asynchronous distributed unmixing procedure based on a recently proposed asynchronous algorithm. Under standard assumptions, the proposed algorithm inherits its convergence properties from recent contributions in nonconvex optimization, while allowing the problem of interest to be efficiently addressed. Comparisons with a distributed synchronous counterpart of the proposed unmixing procedure allow its interest to be assessed on synthetic and real data. Besides, thanks to its genericity and flexibility, the procedure investigated in this paper can be implemented to address various matrix factorization problems.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Sensitivity Analysis of the Standard Deviation of the Copolarized Phase
           Difference for Sea Oil Slick Observation
    • Authors: Andrea Buono;Ferdinando Nunziata;Carina Regina de Macedo;Domenico Velotto;Maurizio Migliaccio;
      Pages: 2022 - 2030
      Abstract: In this paper, a time series of 33 TerraSAR-X copolarized synthetic aperture radar (SAR) imagery collected in Stripmap mode over the Gulf of Mexico in a wide range of incidence angles and sea-state condition is exploited, together with a theoretical framework based on the X-Bragg scattering model, to analyze the effects of noise, angle of incidence, (AOI) and wind speed on the standard deviation of the copolarized phase difference (σΦc) evaluated over sea surface with and without oil slicks. This large data set represents an unprecedented opportunity to analyze, for the first time, the influence of both SAR acquisition and surface parameters on the broadening of the copolarized phase difference probability density function (pdf), pΦc(Φc). Experimental results show that the X-Bragg scattering model, here adopted to predict the sea surface pΦc(Φc), gives an understanding of the increasing trend of σΦc with respect to AOI. It is shown that the noise significantly contributes to broaden pΦc(Φc) over both slick-free and slick-covered sea surface, while the effects of low-to-moderate wind regimes are negligible. In addition, σΦc exhibits a larger sensitivity to the scene variability, if compared to single-polarization intensity channels, over both slick-free and oil-covered sea surface. This sensitivity is more pronounced at lower AOIs due to the higher noise equivalent sigma zero (NESZ) that affects larger AOIs.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Demystifying the Capability of Sublook Correlation Techniques for Vessel
           Detection in SAR Imagery
    • Authors: Christoph H. Gierull;
      Pages: 2031 - 2042
      Abstract: This paper examines the attainable performance of various Doppler sublook or subband cross correlation techniques for vessel detection in synthetic aperture radar images. Research from the past two decades claims that these techniques were capable of improving the detection of small ships in challenging maritime environments. Despite many published experimental examples, a thorough analytical investigation corroborating this claim is noticeably absent. This paper is based on a rigorous theoretical analysis founded on the statistical properties of a textured sea surface model in thermal noise with simultaneous consideration of a constant false alarm rate. Emphasis has been placed on the correct accounting for detrimental physical effects caused by the Doppler spectrum being split into nonoverlapping parts, which have not been sufficiently considered in the literature to date. The theoretical results are confirmed via simulations and are substantiated with real RADARSAT-2 data. The analysis in this paper has neither found theoretical nor empirical evidence that sublook correlation techniques outperform the classical detector based on the image magnitude.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • RoadNet: Learning to Comprehensively Analyze Road Networks in Complex
           Urban Scenes From High-Resolution Remotely Sensed Images
    • Authors: Yahui Liu;Jian Yao;Xiaohu Lu;Menghan Xia;Xingbo Wang;Yuan Liu;
      Pages: 2043 - 2056
      Abstract: It is a classical task to automatically extract road networks from very high-resolution (VHR) images in remote sensing. This paper presents a novel method for extracting road networks from VHR remotely sensed images in complex urban scenes. Inspired by image segmentation, edge detection, and object skeleton extraction, we develop a multitask convolutional neural network (CNN), called RoadNet, to simultaneously predict road surfaces, edges, and centerlines, which is the first work in such field. The RoadNet solves seven important issues in this vision problem: 1) automatically learning multiscale and multilevel features [gained by the deeply supervised nets (DSN) providing integrated direct supervision] to cope with the roads in various scenes and scales; 2) holistically training the mentioned tasks in a cascaded end-to-end CNN model; 3) correlating the predictions of road surfaces, edges, and centerlines in a network model to improve the multitask prediction; 4) designing elaborate architecture and loss function, by which the well-trained model produces approximately single-pixel width road edges/centerlines without nonmaximum suppression postprocessing; 5) cropping and bilinear blending to deal with the large VHR images with finite-computing resources; 6) introducing rough and simple user interaction to obtain desired predictions in the challenging regions; and 7) establishing a benchmark data set which consists of a series of VHR remote sensing images with pixelwise annotation. Different from the previous works, we pay more attention to the challenging situations, in which there are lots of shadows and occlusions along the road regions. Experimental results on two benchmark data sets show the superiority of our proposed approaches.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Statistical Detection Theory Approach to Hyperspectral Image
    • Authors: Chein-I Chang;
      Pages: 2057 - 2074
      Abstract: This paper presents a statistical detection theory approach to hyperspectral image (HSI) classification which is quite different from many conventional approaches reported in the HSI classification literature. It translates a multi-target detection problem into a multi-class classification problem so that the well-established statistical detection theory can be readily applicable to solving classification problems. In particular, two types of classification, a priori classification and a posteriori classification, are developed in corresponding to Bayes detection and maximum a posteriori (MAP) detection, respectively, in detection theory. As a result, detection probability and false alarm probability can also be translated to classification rate and false classification rate derived from a confusion classification matrix used for classification. To evaluate the effectiveness of a posteriori classification, a new a posteriori classification measure, to be called precision rate (PR), is also introduced by MAP classification in contrast to overall accuracy (OA) that can be considered as a priori classification measure and has been used for Bayes classification. The experimental results provide evidence that a priori classifier as Bayes classifier which performs well in terms of OA does not necessarily perform well as a posteriori classifier in terms of PR. That is, PR is the only criterion that can be used as a posteriori classification measure to evaluate how well a classifier performs.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Joint Petrophysical and Structural Inversion of Electromagnetic and
           Seismic Data Based on Volume Integral Equation Method
    • Authors: Tian Lan;Na Liu;Feng Han;Qing Huo Liu;
      Pages: 2075 - 2086
      Abstract: A joint petrophysical and structural inversion method for electromagnetic (EM) and seismic data based on the volume integral equation (VIE) is proposed in this paper. In the forward EM problem, only the contrast of conductivity is solved by the electric field integral equation method. However, in the forward seismic problem, both the contrasts of velocity and mass density are solved by the combined field VIE method. Both forward solvers are accelerated by the fast Fourier transform. In the inversion problem, by using the petrophysical equations about the porosity and saturation and applying the chain rule, we fuse the EM and seismic data and construct the joint petrophysical inversion equations, which can be solved by the variational Born iteration method. Then, in order to further enhance the reconstructed results of the joint petrophysical inversion, we enforce the structural similarity constraint between porosity and water saturation and add the cross-gradient function to the joint petrophysical inversion cost function. Two typical geophysical models based on the remote sensing measurement are used to validate the proposed methods. One is the cross-well model, and the other is the marine surface exploration model. The advantage of the joint inversion compared with the separate inversion is evaluated based on the resolution and the data misfits of the reconstructed profiles as well as the antinoise ability.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Incorporating Full Attenuation Mechanisms of Poroelastic Media for
           Realistic Subsurface Sensing
    • Authors: Mingwei Zhuang;Qiwei Zhan;Jianyang Zhou;Na Liu;Qing Huo Liu;
      Pages: 2087 - 2096
      Abstract: Porous materials are ubiquitous in the subsurface formations of the earth where acoustic and seismic waves are used for remote sensing. However, it is not well understood how the dissipation and the dispersion of poroelastic waves are caused by the viscoelastic and viscous properties of the constituents such as solid grains and pore fluid and by the viscoelastic dissipation of the solid frame, as well as the viscodynamic coupling of the pore fluid to the solid frame due to its global and local flows relative to the solid grains. Such attenuation mechanisms have seldom been incorporated in subsurface sensing simulations, although they can be very important to applications. In this paper, we propose a complete attenuation model, including both full stiffness and viscodynamic dissipation, for poroelastic media in seismic wave simulations. Completely based on a generalized Zener model, the effects associated with physical dissipation and frequency-dependent dispersion are accurately simulated by a finite-difference time-domain algorithm. Verifications with analytical solutions show the accuracy, efficiency, and flexibility of our method. Numerical results demonstrate that the attenuation of Biot's model in the sediment of the seafloor has significant effects on acoustic wave scattering from complex geologic structures.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Wind Speed Estimation Using Acoustic Underwater Glider in a Near-Shore
           Marine Environment
    • Authors: Dorian Cazau;Julien Bonnel;Mark Baumgartner;
      Pages: 2097 - 2106
      Abstract: This paper investigates the use of an acoustic glider to perform acoustical meteorology. This discipline consists of analyzing ocean ambient noise to infer above-surface meteorological conditions. The paper focuses on wind speed estimation, in a near-shore marine environment. In such a shallow water context, the ambient noise field is complex, with site-dependent factors and a variety of nonweather concurrent acoustic sources. A conversion relationship between sound pressure level and wind speed is proposed, taking the form of an outlier-robust nonlinear regression model learned with in situ data. This method is successfully applied to experimental data collected in Massachusetts Bay (MA, USA) during four glider surveys. An average error in wind speed estimation of 1.3 m · s-1 (i.e., average relative error of 14%) over wind speed values up to 17 m · s-1 is reported with this method, which outperformed results obtained with relationships from the literature. Quantitative results are also detailed on the dependence of wind speed error estimation on the environment characteristics, and on the classification performance of observations contaminated by acoustic sources other than wind. Passive acoustic-based weather systems are a promising solution to provide long-term in situ weather data with fine time and spatial resolutions. These data are crucial for satellite calibration and assimilation in meteorological models. From a broader perspective, this paper is the first step toward an operationalization of acoustic weather systems and their on-board embedding in underwater monitoring platforms such as gliders.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • MIMO Ground Penetrating Radar Imaging Through Multilayered Subsurface
           Using Total Variation Minimization
    • Authors: Wenji Zhang;Ahmad Hoorfar;
      Pages: 2107 - 2115
      Abstract: In most of the existing ground penetrating radar (GPR) imaging algorithms, using either full or sparse data collection, the ground is modeled as a single half-space layer. In this paper, a generalized sparse imaging approach with total variation minimization (TVM) for multiple-input multiple-output (MIMO) GPR imaging through multilayered subsurface is proposed. The multilayered media Green's function is incorporated in the imaging algorithm to take into account the complex wave propagation effects under multilayered subsurface. An analytical expression of the layered subsurface Green's function is derived using the stationary-phase method, which significantly reduces the computation time and complexity. On the other hand, as TVM minimizes the gradient of the image, its incorporation in the imaging algorithm results in a reconstruction that preserves the geometry and edges of the targets better than the standard L1-minimization-based sparsity-driven imaging. The number of antenna elements and frequency measurements in MIMO GPR system can be significantly reduced using the proposed technique without degradation of the image quality. Although MIMO configuration is investigated in this paper, the presented approach can be simply applied to monostatic synthetic aperture radar.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Graph-Based Semisupervised Deep Learning Model for PolSAR Image
    • Authors: Haixia Bi;Jian Sun;Zongben Xu;
      Pages: 2116 - 2132
      Abstract: Aiming at improving the classification accuracy with limited numbers of labeled pixels in polarimetric synthetic aperture radar (PolSAR) image classification task, this paper presents a graph-based semisupervised deep learning model for PolSAR image classification. It models the PolSAR image as an undirected graph, where the nodes correspond to the labeled and unlabeled pixels, and the weighted edges represent similarities between the pixels. Upon the graph, we design an energy function incorporating a semisupervision term, a convolutional neural network (CNN) term, and a pairwise smoothness term. The employed CNN extracts abstract and data-driven polarimetric features and outputs class label predictions to the graph model. The semisupervision term enforces the category label constraints on the human-labeled pixels. The pairwise smoothness term encourages class label smoothness and the alignment of class label boundaries with the image edges. Starting from an initialized class label map generated based on K-Wishart distribution hypothesis or superpixel segmentation of PauliRGB images, we iteratively and alternately optimize the defined energy function until it converges. We conducted experiments on two real benchmark PolSAR images, and extensive experiments demonstrated that our approach achieved the state-of-the-art results for PolSAR image classification.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Sparsity Optimization Method for Slow-Moving Landslides Detection in
           Satellite Image Time-Series
    • Authors: Mai Quyen Pham;Pascal Lacroix;Marie Pierre Doin;
      Pages: 2133 - 2144
      Abstract: This paper presents a new method based on recent optimization technique to detect slow-moving landslides (
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Capsule Networks for Hyperspectral Image Classification
    • Authors: Mercedes E. Paoletti;Juan Mario Haut;Ruben Fernandez-Beltran;Javier Plaza;Antonio Plaza;Jun Li;Filiberto Pla;
      Pages: 2145 - 2160
      Abstract: Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral-spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral-spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral-spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral-spatial capsules, which are able to learn HSI spectral-spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted using five well-known HSI data sets and several state-of-the-art classification methods, reveal that our HSI classification approach based on spectral-spatial capsules is able to provide competitive advantages in terms of both classification accuracy and computational time.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Improving Nighttime Light Imagery With Location-Based Social Media Data
    • Authors: Naizhuo Zhao;Wei Zhang;Ying Liu;Eric L. Samson;Yong Chen;Guofeng Cao;
      Pages: 2161 - 2172
      Abstract: Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • An Approach to Improve Leaf Pigment Content Retrieval by Removing Specular
           Reflectance Through Polarization Measurements
    • Authors: Yingying Li;Yaoliang Chen;Jingfeng Huang;
      Pages: 2173 - 2186
      Abstract: Specular reflectance is an important error source in the remote retrieval of leaf pigment content. However, removing this disturbance is challenging because the specular component cannot be directly separated from the leaf reflectance. In this paper, we removed the specular reflectance through polarization measurements at single leaf scale. First, polarization measurements were taken on leaves in the nadir and an oblique viewing direction. Specular reflectance in these two directions was estimated. Second, leaf specular reflectance was subtracted from the total reflectance. Based on the consequent diffuse reflectance, three common types of vegetation indices (VIs), simple ratio (SR), normalized difference (ND), and red edge position (REP) were built to retrieve pigment content. The results show that: 1) specular reflectance interfered with the retrieval. The greater this component, the more serious it caused the disturbance. The retrieval accuracy in forward 35° was much lower than that in the nadir. 2) After specular reflectance removal, the retrieval in both directions was generally improved for the SR and the ND VIs. The improvement in forward 35° was remarkable, with accuracy reaching or approaching the corresponding nadir level. On the two pigments, improvement for chlorophylls was more apparent than for carotenoids. 3) Of all VIs investigated, the REP was insensitive to specular reflectance and had the best resistance capability. Overall, the proposed methods can effectively eliminate specular interference and improve pigment content retrieval. Even under disadvantageous circumstances with strong specular reflectance, the improvement was still reliable.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Marine Oil Slicks Quantification From L-band Dual-Polarization SAR Imagery
    • Authors: Olivier Boisot;Sébastien Angelliaume;Charles-Antoine Guérin;
      Pages: 2187 - 2197
      Abstract: We show, using simple physical models, that a quantitative estimation of the volume fraction of marine oil slicks can be achieved from dual-polarization synthetic aperture radar (SAR) imagery. Volume fraction, which quantifies the proportion of seawater in oil in the case of a mixture, depends primarily on volume scattering mechanisms and is inferred from the polarization ratio in the L-band. A quantification algorithm is derived, namely, the volume fraction estimation algorithm that is applied to two experimental data sets acquired in the Mediterranean Sea during the POLLUPROOF'2015 exercise and in the North Sea during the NOFO'2015 experiment using the Office National d'Études et de Recherches Aérospatiales airborne L-band SETHI system. The resulting volume fraction maps of the quantification method are presented and discussed, opening new perspectives for marine oil slicks monitoring by means of dual-polarization SAR imagery.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Spatiotemporal Subpixel Geographical Evolution Mapping
    • Authors: Da He;Yanfei Zhong;Liangpei Zhang;
      Pages: 2198 - 2220
      Abstract: In recent decades, spatiotemporal subpixel mapping (SSM) approaches have been extensively developed to deal with the mixed-pixel problem by incorporating fine spatial resolution images with the same field of view from different acquisition times. This is an alternative to the conventional subpixel mapping (SPM) method, which is based on only monotemporal images. SSM has become one of the state-of-the-art SPM approaches, and has been widely applied in urban management and ecological monitoring. However, in the traditional SSM methods, the spatial correlation within the multitemporal images is insufficiently exploited and is ignored in the spatiotemporal model construction. In addition, the contribution of the land covers' spatial distribution in the multitemporal images is incompletely considered, and the geographic variation during the time interval is ignored, which underutilizes the spatiotemporal information. In this paper, an SSM algorithm based on a geographically weighted regression (GWR) model and evolutionary algorithm theory, called spatiotemporal subpixel geographical evolution mapping (STGEM), is proposed for multitemporal remote sensing images. The proposed algorithm considers the spatiotemporal dependence not only between the current subpixel and the corresponding fine pixel, but also with the neighboring fine distribution patterns within the fine image. Moreover, the potential temporal information of the geospatial variation is fully realized by considering not only the time interval between the bitemporal images, but also the ratio of changed area between them, based on the GWR model. Two synthetic-image experiments with bitemporal Landsat 8 images and bitemporal QuickBird images were carried out to validate the proposed algorithm. Furthermore, a real-image experiment using a bitemporal pair of Gaofen-2 images and a Landsat 8 image was also undertaken. A comparison was made with several traditional SPM methods, as well as the state-of-the-art SSM ap-roaches, and the experimental results confirmed the superiority of the proposed STGEM algorithm. The proposed STGEM achieves a fine spatial and temporal resolution thematic map, both qualitatively and quantitatively, and has great potential for fine-scale and frequent time-series observation and monitoring.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • The Value of SMAP for Long-Term Soil Moisture Estimation With the Help of
           Deep Learning
    • Authors: Kuai Fang;Ming Pan;Chaopeng Shen;
      Pages: 2221 - 2233
      Abstract: The Soil Moisture Active Passive (SMAP) mission measures important soil moisture data globally. SMAP's products might not always perform better than land surface models (LSM) when evaluated against in situ measurements. However, we hypothesize that SMAP presents added value for long-term soil moisture estimation in a data fusion setting as evaluated by in situ data. Here, with the help of a time series deep learning (DL) method, we created a seamlessly extended SMAP data set to test this hypothesis and, importantly, gauge whether such benefits extend to years beyond SMAP's limited lifespan. We first show that the DL model, called long short-term memory (LSTM), can extrapolate SMAP for several years and the results are similar to the training period. We obtained prolongation results with low-performance degradation where SMAP itself matches well with in situ data. Interannual trends of root-zone soil moisture are surprisingly well captured by LSTM. In some cases, LSTM's performance is limited by SMAP, whose main issue appears to be its shallow sensing depth. Despite this limitation, a simple average between LSTM and an LSM Noah frequently outperforms Noah alone. Moreover, Noah combined with LSTM is more skillful than when it is combined with another LSM. Over sparsely instrumented sites, the Noah-LSTM combination shows a stronger edge. Our results verified the value of LSTM-extended SMAP data. Moreover, DL is completely data driven and does not require structural assumptions. As such, it has its unique potential for long-term projections and may be applied synergistically with other model-data integration techniques.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Heuristic-Learning Optimizer for Elastodynamic Waveform Inversion in
           Passive Seismics
    • Authors: Ismael A. Vera Rodriguez;
      Pages: 2234 - 2248
      Abstract: This paper explores (full-) waveform inversion in passive seismics to simultaneously optimize the source parameters of seismic events together with the properties of the medium of wave propagation. A heuristic optimization algorithm inspired in iterative design is proposed, which incorporates ideas from particle swarm optimization and variable projection. The algorithm is designed to accelerate convergence, improve stability and robustness, and minimize the number of user-defined parameters. The performance of the algorithm is illustrated with a real data example of hydraulic stimulation monitoring using a surface array of seismic receivers. In this example, only the locations of the receivers are known. The inversion is targeted to jointly optimize the parameters of a viscoelastic velocity model, associated models of receiver residual statics, wavelet signatures for direct compressional and shear arrivals, spatiotemporal locations of the input seismic events, and their corresponding moment tensors. Velocity model and locations are compared to estimations of the same parameters previously obtained through a well-established workflow using decoupled inversions. Results from both approaches show consistency. The main advantage of the joint approach is, therefore, the possibility of incorporating additional unknowns into the optimization to obtain self-consistent solutions using a single inversion process.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Hybrid-Sparsity Constrained Dictionary Learning for Iterative Deblending
           of Extremely Noisy Simultaneous-Source Data
    • Authors: Shaohuan Zu;Hui Zhou;Rushan Wu;Weijian Mao;Yangkang Chen;
      Pages: 2249 - 2262
      Abstract: Simultaneous-source acquisition, breaking the limit of conventional seismic acquisition, is a rapidly evolving research field, due to its advantage in reducing survey time and improving data quality. The benefits of simultaneous-source acquisition are compromised by the intense blending interference. Separating a blended record into a group of individual records, known as “deblending” is one of the most popular solution to the problem. However, the blended records are often corrupted by random noise, which causes difficulties in separation. In an iterative deblending algorithm, the incoherent interference can be simulated and subtracted from the blended record. When the random noise is strong, it is difficult to simulate the incoherent interference. In this paper, we propose a hybrid-sparsity constraint model that applies the dictionary learning into the deblending framework that is based on the sparsity-promoting transform to deal with extremely noisy simultaneous source data. The dictionary learning with fine-tuned adaptation can learn the incoherent interference into atoms and reject random noise. Then, the sparse transform-based framework is implemented to iteratively separate the signal and interference. We use two synthetic examples to demonstrate the advantage of the proposed method in extremely noisy situations. Two field examples further confirm the superior deblending performance of the proposed method for the noisy simultaneous-source data over the curvelet transform-based and rank reduction-based methods.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Hyperspectral Anomaly Detection via Background and Potential Anomaly
           Dictionaries Construction
    • Authors: Ning Huyan;Xiangrong Zhang;Huiyu Zhou;Licheng Jiao;
      Pages: 2263 - 2276
      Abstract: In this paper, we propose a new anomaly detection method for hyperspectral images based on two well-designed dictionaries: background dictionary and potential anomaly dictionary. In order to effectively detect an anomaly and eliminate the influence of noise, the original image is decomposed into three components: background, anomalies, and noise. In this way, the anomaly detection task is regarded as a problem of matrix decomposition. Considering the homogeneity of background and the sparsity of anomalies, the low-rank and sparse constraints are imposed in our model. Then, the background and potential anomaly dictionaries are constructed using the background and anomaly priors. For the background dictionary, a joint sparse representation (JSR)-based dictionary selection strategy is proposed, assuming that the frequently used atoms in the overcomplete dictionary tend to be the background. In order to make full use of the prior information of anomalies hidden in the scene, the potential anomaly dictionary is constructed. We define a criterion, i.e., the anomalous level of a pixel, by using the residual calculated in the JSR model within its local region. Then, it is combined with a weighted term to alleviate the influence of noise and background. Experiments show that our proposed anomaly detection method based on potential anomaly and background dictionaries construction can achieve superior results compared with other state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Unsupervised Difference Representation Learning for Detecting Multiple
           Types of Changes in Multitemporal Remote Sensing Images
    • Authors: Puzhao Zhang;Maoguo Gong;Hui Zhang;Jia Liu;Yifang Ban;
      Pages: 2277 - 2289
      Abstract: With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation of spatial-temporal images. Multitype change detection (CD) is a significant research topic in multitemporal remote sensing image analysis, and its core is to effectively measure the difference degree and represent the difference among the multitemporal images. In this paper, we propose a novel difference representation learning (DRL) network and present an unsupervised learning framework for multitype CD task. Deep neural networks work well in representation learning but rely too much on labeled data, while clustering is a widely used classification technique free from supervision. However, the distribution of real remote sensing data is often not very friendly for clustering. To better highlight the changes and distinguish different types of changes, we combine difference measurement, DRL, and unsupervised clustering into a unified model, which can be driven to learn Gaussian-distributed and discriminative difference representations for nonchange and different types of changes. Furthermore, the proposed model is extended into an iterative framework to imitate the bottom-up aggregative clustering procedure, in which similar change types are gradually merged into the same classes. At the same time, the training samples are updated and reused to ensure that it converges to a stable solution. The experimental studies on four pairs of multispectral data sets demonstrate the effectiveness and superiority of the proposed model on multitype CD.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Deep Few-Shot Learning for Hyperspectral Image Classification
    • Authors: Bing Liu;Xuchu Yu;Anzhu Yu;Pengqiang Zhang;Gang Wan;Ruirui Wang;
      Pages: 2290 - 2304
      Abstract: Deep learning methods have recently been successfully explored for hyperspectral image (HSI) classification. However, training a deep-learning classifier notoriously requires hundreds or thousands of labeled samples. In this paper, a deep few-shot learning method is proposed to address the small sample size problem of HSI classification. There are three novel strategies in the proposed algorithm. First, spectral-spatial features are extracted to reduce the labeling uncertainty via a deep residual 3-D convolutional neural network. Second, the network is trained by episodes to learn a metric space where samples from the same class are close and those from different classes are far. Finally, the testing samples are classified by a nearest neighbor classifier in the learned metric space. The key idea is that the designed network learns a metric space from the training data set. Furthermore, such metric space could generalize to the classes of the testing data set. Note that the classes of the testing data set are not seen in the training data set. Four widely used HSI data sets were used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can achieve better classification accuracy than the conventional semisupervised methods with only a few labeled samples.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Centroid and Covariance Alignment-Based Domain Adaptation for Unsupervised
           Classification of Remote Sensing Images
    • Authors: Li Ma;Melba M. Crawford;Lei Zhu;Yong Liu;
      Pages: 2305 - 2323
      Abstract: A new domain adaptation algorithm based on the class centroid and covariance alignment (CCCA) is proposed for classification of remote sensing images. This approach exploits both the first- and second-order statistics to describe the data distribution and aligns the data distribution between domains on a per-class basis. Since the predicted labels of target data are used to estimate the two statistics, we applied overall centroid alignment (OCA) as a coarse domain adaptation strategy to improve the estimation accuracy. In addition, the OCA coarse adaptation in conjunction with CCCA refined adaptation can also benefit by incorporation of spatial information, resulting in a Spa_OCA_CCCA approach. The proposed approach is easy to implement, and only one parameter is required in the spatial filtering step. It does not require labeled information in the target domain and can achieve labor-free classification. The experimental results using Hyperion, National Center for Airborne Laser Mapping, and Worldview-2 remote sensing images demonstrated the effectiveness of the proposed approach.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Fully Automatic Spectral–Spatial Fuzzy Clustering Using an Adaptive
           Multiobjective Memetic Algorithm for Multispectral Imagery
    • Authors: Yuting Wan;Yanfei Zhong;Ailong Ma;
      Pages: 2324 - 2340
      Abstract: Clustering of remote sensing imagery is a tough task due to the particular and complex structure of remote sensing images and the shortage of known information. In this paper, we propose a fully automatic spectral-spatial fuzzy clustering method using an adaptive multiobjective memetic algorithm (AMOMA) for multispectral remote sensing imagery. This approach is made up of two automatic layers: an automatic determination layer and an automatic clustering layer. The first layer seeks the optimal number of clusters through a self-adaptive differential evolution algorithm. The second layer then takes advantage of the AMOMA for spectral-spatial clustering using the optimal number of clusters. The knee point from the Pareto front is then selected through the angle-based method in every generation, and we then compare the knee points between generations to output the final optimal solution. The effectiveness of the proposed method is verified by the experimental results obtained with three remote sensing data sets.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor
    • Authors: Fengchao Xiong;Yuntao Qian;Jun Zhou;Yuan Yan Tang;
      Pages: 2341 - 2357
      Abstract: Hyperspectral unmixing decomposes a hyperspectral imagery (HSI) into a number of constituent materials and associated proportions. Recently, nonnegative tensor factorization (NTF)-based methods have been proposed for hyperspectral unmixing thanks to their capability in representing an HSI without any information loss. However, tensor factorization-based HSI processing approaches often suffer from low-signal-to-noise ratio condition of HSI and nonuniqueness of the solution. This problem can be effectively alleviated by introducing various spatial constraints into tensor factorization to suppress the noise and decrease the number of extreme, stationary, and saddle points. On the other hand, total variation (TV) adaptively promotes piecewise smoothness while preserving edges. In this paper, we propose a TV regularized matrix-vector NTF method. It takes advantage of tensor factorization in preserving global spectral-spatial information and the merits of TV in exploiting local spatial information, thus generating smooth abundance maps with preserved edges. Experimental results on synthetic and real-world data show that the proposed method outperforms the state-of-the-art methods.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Constrained Sparse Representation Model for Hyperspectral Anomaly
    • Authors: Qiang Ling;Yulan Guo;Zaiping Lin;Wei An;
      Pages: 2358 - 2371
      Abstract: In this paper, we propose a novel sparsity-based algorithm for anomaly detection in hyperspectral imagery. The algorithm is based on the concept that a background pixel can be approximately represented as a sparse linear combination of its spatial neighbors while an anomaly pixel cannot if the anomalies are removed from its neighborhood. To be physically meaningful, the sum-to-one and nonnegativity constraints are imposed to abundance vector based on the linear mixture model, and the upper bound constraint on sparsity level is removed for better recovery of the test pixel. First, the proposed method utilizes the redundant background information to automatically remove anomalies from the background dictionary. Then, the reconstruction error obtained by the new background dictionary is directly used for anomaly detection. Moreover, a kernel version of the proposed method is also derived to completely exploit the nonlinear feature of hyperspectral data. An important advantage of the proposed methods is their capability to adaptively model the background even when some anomaly pixels are involved. Extensive experiments have been conducted on three real hyperspectral data sets. It is demonstrated that the proposed detectors achieve a promising detection performance with a relatively low computational cost.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • $2.12~mutext{m}$+ +Bands+of+MODIS&rft.title=Geoscience+and+Remote+Sensing,+IEEE+Transactions+on&rft.issn=0196-2892&;&rft.aufirst=Zhongting&;Pengfei+Ma;Hui+Chen;Yuhuan+Zhang;Lijuan+Zhang;Shenshen+Li;Qing+Li;Liangfu+Chen;">Aerosol Retrieval in the Autumn and Winter From the Red and
           $2.12~mutext{m}$ Bands of MODIS
    • Authors: Zhongting Wang;Pengfei Ma;Hui Chen;Yuhuan Zhang;Lijuan Zhang;Shenshen Li;Qing Li;Liangfu Chen;
      Pages: 2372 - 2380
      Abstract: In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing-Tianjin-Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared (2.12 μm) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and 2.12 μm bands is invariant. With MODIS data over the Beijing-Tianjin-Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and 2.12 μm bands can lead to retrieved errors less than 0.1.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Omega-K Algorithm for Near-Field 3-D Image Reconstruction Based on Planar
           SIMO/MIMO Array
    • Authors: Kai Tan;Shiyou Wu;Xiaojun Liu;Guangyou Fang;
      Pages: 2381 - 2394
      Abstract: The characteristics of the range cell migration (RCM) under single-input-multiple-output (SIMO) bi-static geometry are studied and an omega-K algorithm for near-field 3-D imaging based on planar SIMO or multiple-input-multiple-output (MIMO) array is proposed. The RCM and the linear part of the range offset (RO) within SIMO data are corrected by employing a 3-D Stolt transformation. The residual range error is further compensated by phase multiplication and an image-domain interpolation. Reconstruction for a MIMO aperture can be achieved by adding together all the focusing results from its SIMO subarrays. The implementation details of the algorithm are described. The imaging resolution and the sampling requirements for a MIMO aperture are discussed. Since the RO correction is achieved by an approximate way, the spatial limitation for accurate reconstruction is also given. The imaging accuracy and the high efficiency of the algorithm are demonstrated both by simulations and experiments with various distributed targets based on planar MIMO arrays. Real-time 3-D imaging for planar SIMO/MIMO aperture is expected to be achieved by using the algorithm.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Imaging With 3-D Aperture Synthesis Radiometers
    • Authors: Li Feng;Qingxia Li;Yufang Li;
      Pages: 2395 - 2406
      Abstract: The spatial resolution is still a problem in passive microwave remote sensing, especially in low frequency. In recent years, the satellite formation flying has been proposed. Based on this technique, a large array is able to be synthesized in orbit to achieve higher spatial resolution. However, it is a big challenge for the control system to constrain all the satellites in a coplane in orbit. The 3-D array configuration is a good choice for a synthesized array based on satellite formation flying. In this paper, the complete formulation of visibility functions, including system imperfections, in a 3-D aperture synthesis radiometer (3-D ASR) is derived. The array factor of a 3-D ASR is defined. The reconstructed modified brightness temperature (BT) is a 3-D linear convolution of the modified BT and the array factor. Based on this relationship, the reconstruction method for a practical 3-D ASR is studied. The numerical results demonstrate that the reconstruction method is correct and stable. Finally, a discussion is given to analyze several existing methods that were proposed to reconstruct BT image for 3-D arrays in radio astronomy and earth observation. Compared with these existing methods, our imaging method is more suitable for earth observation based on the technique of satellites formation flying in low earth orbit. In addition, according to the derivations, some mature techniques that were developed for 2-D ASRs may be applied to 3-D ASRs.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • A Novel Semicoupled Projective Dictionary Pair Learning Method for PolSAR
           Image Classification
    • Authors: Yanqiao Chen;Licheng Jiao;Yangyang Li;Lingling Li;Dan Zhang;Bo Ren;Naresh Marturi;
      Pages: 2407 - 2418
      Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in remote sensing image processing. In recent years, stacked auto-encoder (SAE) has obtained a series of excellent results in PolSAR image classification. The recently proposed projective dictionary pair learning (DPL) model takes both accuracy and time consumption into consideration, and another recently proposed semicoupled dictionary learning (SCDL) model gives a new way to fit different features. Based on the SAE, DPL, and SCDL models, we propose a novel semicoupled projective DPL method with SAE (SAE-SDPL) for PolSAR image classification. Our method can get the classification result efficiently and correctly and meanwhile giving a new method to fit different features. In this paper, three PolSAR images are used to test the performance of SAE-SDPL. Compared with some state-of-the-art methods, our method obtains excellent results in PolSAR image classification.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Joint-Sparse-Blocks and Low-Rank Representation for Hyperspectral Unmixing
    • Authors: Jie Huang;Ting-Zhu Huang;Liang-Jian Deng;Xi-Le Zhao;
      Pages: 2419 - 2438
      Abstract: Hyperspectral unmixing has attracted much attention in recent years. Single sparse unmixing assumes that a pixel in a hyperspectral image consists of a relatively small number of spectral signatures from large, ever-growing, and available spectral libraries. Joint-sparsity (or row-sparsity) model typically enforces all pixels in a neighborhood to share the same set of spectral signatures. The two sparse models are widely used in the literature. In this paper, we propose a joint-sparsity-blocks model for abundance estimation problem. Namely, the abundance matrix of size m × n is partitioned to have one row block and s column blocks and each column block itself is joint-sparse. It generalizes both the single (i.e., s = n) and the joint (i.e., s = 1) sparsities. Moreover, concatenating the proposed joint-sparsity-blocks structure and low rankness assumption on the abundance coefficients, we develop a new algorithm called joint-sparseblocks and low-rank unmixing. In particular, for the joint-sparseblocks regression problem, we develop a two-level reweighting strategy to enhance the sparsity along the rows within each block. Simulated and real-data experiments demonstrate the effectiveness of the proposed algorithm.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Multifrequency 3-D Inversion of GREATEM Data by BCGS-FFT-BIM
    • Authors: Chen Qiu;Bingyang Liang;Feng Han;Hai Liu;Chunhui Zhu;Na Liu;Fubo Liu;Guangyou Fang;Qing Huo Liu;
      Pages: 2439 - 2448
      Abstract: A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
  • Striping Noise Mitigation for Tropical Rainfall Measuring Mission
           Microwave Imager Observations
    • Authors: Huijie Dong;Xiaolei Zou;
      Pages: 2449 - 2463
      Abstract: The conical-scanning Tropical Rainfall Measuring Mission Microwave Imager (TMI), which plays an important role in monitoring global precipitation, has nine channels. A clear striping noise feature is found in pitch-over maneuver data of deep-space and earth-view observations at all nine channels of the TMI. A modified striping noise mitigation algorithm is used to extract the striping noise from deep-space and earth-scene observations. The magnitude of the striping noise extracted from TMI channel 10.65 V, 10.65 H, and 37.0 H measurements is about 0.4 K and more than 1.5 K for channel 85.5 V and 85.5 H measurements. After striping noise mitigation, no visible striping feature is seen in the observation-minus-simulation distributions of both deep-space and earth-scene brightness temperatures. Comparisons of the liquid water path (LWP) retrieved from TMI brightness temperatures with and without striping noise mitigation show that the striping noise in brightness temperatures results in a striping feature in LWP retrievals. When the striping noise is not removed from TMI observations, LWP retrievals exhibit a cross-track striping feature. The striping noise leads to a greater retrieval difference in larger LWP areas.
      PubDate: April 2019
      Issue No: Vol. 57, No. 4 (2019)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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