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  Subjects -> ELECTRONICS (Total: 175 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Acta Electronica Malaysia     Open Access  
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 5)
Advances in Electronics     Open Access   (Followers: 76)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 13)
Advances in Power Electronics     Open Access   (Followers: 33)
Advancing Microelectronics     Hybrid Journal  
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 305)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 9)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 9)
Archives of Electrical Engineering     Open Access   (Followers: 13)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 28)
Bioelectronics in Medicine     Hybrid Journal  
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 12)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 44)
China Communications     Full-text available via subscription   (Followers: 8)
Chinese Journal of Electronics     Hybrid Journal  
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 253)
Edu Elektrika Journal     Open Access   (Followers: 1)
Electrica     Open Access  
Electronic Design     Partially Free   (Followers: 104)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 4)
Electronics     Open Access   (Followers: 85)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 10)
Electronics For You     Partially Free   (Followers: 91)
Electronics Letters     Hybrid Journal   (Followers: 26)
Elkha : Jurnal Teknik Elektro     Open Access  
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 50)
Energy Harvesting and Systems     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 2)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 11)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 6)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 185)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IACR Transactions on Symmetric Cryptology     Open Access  
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 96)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 77)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 46)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 9)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 65)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 69)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 55)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 19)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 39)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 19)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 26)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 70)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 11)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 5)
IET Cyber-Physical Systems : Theory & Applications     Open Access   (Followers: 1)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 35)
IET Nanodielectrics     Open Access  
IET Power Electronics     Hybrid Journal   (Followers: 45)
IET Smart Grid     Open Access  
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 11)
IETE Technical Review     Open Access   (Followers: 13)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 3)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 57)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 24)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription   (Followers: 1)
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 12)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 10)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 5)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 6)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 7)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 14)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 12)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 4)
International Journal of Power Electronics     Hybrid Journal   (Followers: 24)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 10)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
JAREE (Journal on Advanced Research in Electrical Engineering)     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 23)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electrical, Electronics and Informatics     Open Access  
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 8)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 6)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 4)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Hybrid Journal   (Followers: 162)
Journal of Information and Telecommunication     Open Access   (Followers: 1)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 7)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 9)
Journal of Microelectronics and Electronic Packaging     Hybrid Journal  
Journal of Microwave Power and Electromagnetic Energy     Hybrid Journal  
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 10)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 4)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 28)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 11)
Journal of Semiconductors     Full-text available via subscription   (Followers: 5)
Journal of Sensors     Open Access   (Followers: 26)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal Rekayasa Elektrika     Open Access  
Jurnal Teknik Elektro     Open Access  
Kinetik : Game Technology, Information System, Computer Network, Computing, Electronics, and Control     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 12)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 18)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 6)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 15)
Paladyn. Journal of Behavioral Robotics     Open Access   (Followers: 1)
Power Electronics and Drives     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 9)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 5)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 53)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 75)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 13)
Solid-State Electronics     Hybrid Journal   (Followers: 9)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 3)
Technical Report Electronics and Computer Engineering     Open Access  
TELE     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 6)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Journal Prestige (SJR): 1.547
Citation Impact (citeScore): 4
Number of Followers: 53  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1939-1404
Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • IEEE Geoscience and Remote Sensing Societys
    • 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: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • IEEE Geoscience and Remote Sensing Societys
    • 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: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Institutional Listings
    • Abstract: Presents a listing of institutions relevant for this issue of the publication.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Foreword to the Special Issue on Cyclone Global Navigation Satellite
           System (CYGNSS) Early on Orbit Performance
    • Authors: C. Ruf;E. Cardellach;M. P. Clarizia;C. Galdi;S. T. Gleason;S. Paloscia;
      Pages: 3 - 6
      Abstract: The papers in this special section examine the orbit performance pf the Cyclone Global Navigation Satellite System (CYGNSS), a constellation of eight satellites that were successfully launched on December 15, 2016 into low earth orbit. Each satellite carries a four-channel bistatic radar receiver that measures GPS signals scattered by the earth surface. Global Navigation Satellite System Reflectometry (GNSS-R) techniques are used to retrieve near surface wind speed over ocean and soil moisture over land. The measurements are unique in several respects, most notably in their ability to penetrate through high levels of precipitation, made possible by the low frequency at which GPS operates, and in the frequent revisit time and complete sampling of the diurnal cycle, made possible by the large number of satellites in a low inclination orbit. Engineering commissioning of the constellation was successfully completed in March 2017. Since then, CYGNSS science data products have been continuously produced.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Assessment of the Differential Drag Maneuver Operations on the CYGNSS
    • Authors: Charles D. Bussy-Virat;Aaron J. Ridley;Abhay Masher;Kyle Nave;Marissa Intelisano;
      Pages: 7 - 15
      Abstract: The CYGNSS constellation was deployed in December 2016. To optimize the coverage of tropical cyclones, the eight observatories have to be evenly spaced out along the orbit. Since the spacecraft do not have a system of propulsion, differential drag maneuvers have been conducted to control the constellation phasing. However, many constraints have considerably restricted the differential drag operations, particularly during the first months of the mission. The study discusses these constraints and provides an evaluation of the effectiveness of the differential drag maneuvers during the first year of the mission. Future plans for the completion of the constellation phasing are also presented.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Relationship Between Temporal and Spatial Resolution for a Constellation
           of GNSS-R Satellites
    • Authors: Charles D. Bussy-Virat;Christopher S. Ruf;Aaron J. Ridley;
      Pages: 16 - 25
      Abstract: Constellations of GNSS-R satellites improve the coverage of regions of interest by repeating measurements in a shorter period of time than with a single spacecraft. However, the temporal and spatial resolution of the samples are dependent on each other. Detecting short time scale changes is generally done with coarser spatial resolution. Likewise, detailed observations of a region with small-scale features require longer intervals of time between observations. This study demonstrates the relationship between temporal and spatial resolution and its dependence on key mission design parameters such as the number of satellites, the number of orbit planes, and their inclination.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Design and Performance of a GPS Constellation Power Monitor System for
           Improved CYGNSS L1B Calibration
    • Authors: Tianlin Wang;Christopher S. Ruf;Bruce Block;Darren S. McKague;Scott Gleason;
      Pages: 26 - 36
      Abstract: The Cyclone Global Navigation Satellite System (CYGNSS) uses a bistatic radar configuration with the Global Positioning System (GPS) constellation as the active sources and the CYGNSS satellites as the passive receivers. The GPS effective isotropic radiated power (EIRP), defined as the product of transmit power and antenna gain pattern, is of great importance to the accurate Level 1B calibration of the CYGNSS mission. To address the uncertainties in EIRP, a ground-based GPS constellation power monitor (GCPM) system has been built to accurately and precisely measure the direct GPS signals. A PID thermal controller successfully stabilizes the system temperature over the long term. Radiometric calibration is performed to determine the system dynamic range and to calibrate GCPM gain. Single PRN calibration using a GPS signal simulator is used to compute the scale factor to convert the received counts into power in watts. The GCPM received power is highly repeatable and has been verified with DLR/GSOC's independent measurements. The transmit power (L1 C/A) of the full GPS constellation is estimated using an optimal search algorithm. Updated values for transmit power have been successfully applied to CYGNSS L1B calibration and found to significantly reduce the PRN dependence of CYGNSS L1 and L2 data products.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • The CYGNSS Level 1 Calibration Algorithm and Error Analysis Based on
           On-Orbit Measurements
    • Authors: Scott Gleason;Christopher S. Ruf;Andrew J. O’Brien;Darren S. McKague;
      Pages: 37 - 49
      Abstract: The calibration algorithm used by the Cyclone Global Navigation Satellite System (CYGNSS) mission to produce version 2.1 of its Level 1 (L1) science data products is described. Changes and improvements have been made to the algorithm, relative to earlier versions, based on the first year of on-orbit result. The L1 calibration consists of two parts: first, the Level 1a (L1a) calibration converts the raw Level 0 delay Doppler maps (DDMs) of processed counts into received power in units of watts. Second, the L1a DDMs are then converted to Level 1b DDMs of bistatic radar cross section values by unwrapping the forward scattering model, which are then normalized by the surface scattering area to arrive an observation of $ sigma _0$. An update to the bottom up term-by-term error analysis is also presented, using on-orbit results to better quantify the accuracy of the rolled-up L1 calibration. The error analysis considers uncertainties in all known input calibration parameters. Finally, a method for calibrating the time delay of CYGNSS measurements is presented.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • An Assessment of CYGNSS Normalized Bistatic Radar Cross Section
    • Authors: Faozi Saïd;Zorana Jelenak;Paul S. Chang;Seubson Soisuvarn;
      Pages: 50 - 65
      Abstract: A cyclone global navigation satellite system (CYGNSS) σo calibration analysis is presented using version 2.0 of the Level 1 dataset available on PO.DAAC. Three separate analyzes are conducted, namely, an examination of the specular bin location (in delay) and σo relationship, an investigation of the impact of recently improved characterizations of the GPS effective isotropically radiated power on CYGNSS σo, and an intersatellite σo calibration analysis. We first noted a correlation between the specular delay bin location and σo, where an increase in the specular delay bin resulted in an increase in σo regardless of the wind speed level; a specular delay bin location ranging from 4.75 to 5.00 and from 7.00 to 7.25 resulted in a 14.74 and 17.72 dB median σo, respectively. Noticeable improvements in the median σo were present in the version 2.0 dataset, when separating the data by GPS block type: for blocks IIR, IIF, and IIR-M, median σo were 15.39, 15.42, and 15.10 dB, respectively (compared to 19.38, 20.53, and 21.38 dB in version 1.1). Finally, an unexpected correlation between the instrument noise floor and σo was observed for all eight observatories while conducting the intersatellite σo calibration analysis. Approximately 0-1 dB absolute σo difference biases (with up to ~1 dB standard deviation) between spacecrafts were observed. A report of this analysis was presented to CYGNSS scientists and engineers, who eventually found an issue with the D/A DDM scaling algorithm. We expect better statistical performances in future releases of the data.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Development of the CYGNSS Geophysical Model Function for Wind Speed
    • Authors: Christopher S. Ruf;Rajeswari Balasubramaniam;
      Pages: 66 - 77
      Abstract: Geophysical model functions (GMFs) are developed which map the Level 1 observables made by the Cyclone Global Navigation Satellite System (CYGNSS) radar receivers to ocean surface wind speed. The observables are: 1) the normalized bistatic radar cross section (σo) of the ocean surface; and 2) the slope of the leading edge of the radar return pulse scattered by the ocean surface. GMFs are empirically derived from measurements by CYGNSS which are nearly coincident with independent estimates of the 10-m-referenced ocean surface wind speed (u10). Two different sources of “ground truth” wind speed are considered: numerical weather prediction model outputs and measurements by the NOAA P-3 hurricane hunter during eyewall penetrations of major hurricanes. The GMFs derived in each case have significant differences that are believed to result from differences in the state of development of the long wave portion of the ocean surface height spectrum that result from characteristic differences in wave age and fetch length near versus far from a hurricane.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Ocean Wind Speed Estimation From the GNSS Scattered Power Function Volume
    • Authors: Generoso Giangregorio;Pia Addabbo;Carmela Galdi;Maurizio di Bisceglie;
      Pages: 78 - 86
      Abstract: A model-based procedure for ocean wind speed estimation using global navigation satellite system reflectometry is presented. The method is based on the least-squares matching between the measured scattered power function volume and the volume of the Zavorotny-Voronovich wind-dependent scattered power model. Geometric terms depending on the orbit path as well as the antenna pattern and known propagation losses are considered in the model. Error sources are investigated, and their impact on wind speed estimates is evaluated and minimized. The performance of the proposed algorithm is assessed by simulating delay-Doppler maps in a realistic ocean scattering scenario with Cyclone Global Navigation Satellite System (CYGNSS) observatories, whereas the validation of the algorithm is carried out by comparisons of retrievals from real delay-Doppler maps collected by the space CYGNSS observatories and ground truth data processed within the collaborative NASA CYGNSS Science Team.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Assessment of CYGNSS Wind Speed Retrieval Uncertainty
    • Authors: Christopher S. Ruf;Scott Gleason;Darren S. McKague;
      Pages: 87 - 97
      Abstract: Measurements of near surface wind speed made by the Cyclone Global Navigation Satellite System (CYGNSS) constellation of GNSS-R satellites are evaluated and their uncertainty is assessed in two ways. A bottom-up assessment begins with a model for the error in engineering measurements and propagates that error through the wind speed retrieval algorithm analytically. A top-down assessment performs a statistical comparison between CYGNSS measurements and coincident “ground truth” measurements of wind speed. Results of the two approaches are compared. Overall performance, as determined by the top-down method, is decomposed using the bottom-up approach into its contributing sources of error. Overall root mean square (RMS) uncertainty in the CYGNSS retrievals is 1.4 m/s at wind speeds below 20 m/s. At higher wind speeds, an increase in the retrieval error is primarily caused by a decrease in the sensitivity of the ocean scattering cross section to changes in wind speed. In tropical cyclones, retrieval errors are compounded by unaccounted departures from a fully developed sea state. Overall RMS uncertainty in the CYGNSS retrievals is 17% at wind speeds above 20 m/s.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Using “Rapid Revisit” CYGNSS Wind Speed Measurements to
           Detect Convective Activity
    • Authors: Jeonghwan Park;Joel T. Johnson;Yuchan Yi;Andrew J. O’Brien;
      Pages: 98 - 106
      Abstract: The Cyclone Global Navigation Satellite System (CYGNSS) is a spaceborne GNSS-reflectometry mission, which was launched on December 15, 2016 for ocean surface wind speed measurement. CYGNSS includes eight small satellites in the same low earth orbit, so that the mission provides wind speed products having unprecedented coverage both in time and space to study multitemporal behaviors of oceanic winds. The nature of CYGNSS coverage results in some locations on earth experiencing multiple wind speed measurements within a short period of time (a “clump” of observations in time) resulting in a “rapid revisit” series of measurements. Such observations seemingly can provide indications of regions experiencing rapid changes in wind speeds, and therefore serve as an indicator of convective activity. An initial investigation of this concept using simulated and on-orbit CYGNSS measurements is provided in this paper. The temporally “clumped” properties of CYGNSS measurements are examined, and the results show that clump durations and spacing vary with latitude. For example, the duration of a clump can extend as long as a few hours at higher latitudes, with gaps between clumps ranging from 6 to as high as 12 h depending on latitude. Initial examples are provided to indicate the potential of changes within a clump to detect convective activity through a comparison with convective activity indicators derived from model datasets. The results at present are limited by the ongoing calibration of CYGNSS wind speed retrievals, so that future work will be required to obtain a more complete assessment, but nevertheless clearly indicate the potential utility of the method for studies of atmospheric convection.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Sensitivity of CyGNSS Bistatic Reflectivity and SMAP Microwave Radiometry
           Brightness Temperature to Geophysical Parameters Over Land Surfaces
    • Authors: Hugo Carreno-Luengo;Guido Luzi;Michele Crosetto;
      Pages: 107 - 122
      Abstract: This paper presents an assessment of the correlation between CyGNSS-derived global navigation satellite systems reflectometry (GNSS-R) bistatic reflectivity, Γrl, and soil moisture active passive (SMAP) derived brightness temperature, TI/2, over land surfaces. This parametric study is performed as a function of soil moisture content (SMC), vegetation opacity r, and albedo w. Several target areas, classified according to the International Geosphere-Biosphere Program (IGBP) land cover types, are selected to evaluate potential differentiated geophysical effects on “active” (as many transmitters as navigation satellites are in view) and passive approaches. Although microwave radiometry has potentially a better sensitivity to SMC, the spatial resolution achievable from a spaceborne platform is poor, ~40 km. On the other hand, GNSS-R bistatic coherent radar pixel-size is limited by half of the first Fresnel zone, which provides about ~150 m of spatial resolution (depending on the geometry). The main objective of this “active”/passive combination is twofold: a) downscaling the SMC, b) complement the information of microwave radiometry with GNSS-R data to improve the accuracy in SMC determination. The Pearson linear correlation coefficient of Γrl and T I/2 obtained over Thailand, Argentinian Pampas, and Amazon is ~-0.87, ~-0.7, and ~-0.26, respectively, while the so-called tau-omega model is used to fit the data. Results over croplands are quite promising and deserve special attention since the use of GNSS-R could benefit agricultural and hydrological applications because of: a) the high spatio-temporal sampling properties, b) the high spatial resolution, and c) the potential combination with microwave radiometry to improve the accuracy of the measurements.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • A Novel Multitemporal Cloud and Cloud Shadow Detection Method Using the
           Integrated Cloud Z-Scores Model
    • Authors: Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie;Liping Lei;
      Pages: 123 - 134
      Abstract: Accurate cloud and cloud shadow detection in multispectral remote sensing imager due to the high spectral variations of clouds and the complexities of underlying landscapes, especially for images without thermal bands. In this paper, we proposed a multitemporal integrated cloud z-score (MTICZ) method for cloud and cloud shadow detection for multitemporal optical images. First, an integrated cloud z-score (ICZ) index was designed to identify clouds and cloud shadows, and measure the likelihood of a pixel being either a cloudy or shadowed pixel. Clouds and cloud shadows were then detected by differencing the ICZ values between the reference image and the cloudy image, and they were refined using a cloud and shadow matching algorithm. Finally, the MTICZ method was evaluated through cloud fraction estimation and 3,000 random validation samples from the cloudy Landsat scenes. The results indicate that MTICZ method achieved a significant agreement with reference cloud fractions (R2 = 0.97, RMSE = 4.31% ) and performed well under complicated land surface conditions, with an average overall accuracy of 91.65%. In addition, the MTICZ method was compared with two popular cloud detection methods, the Fmask method and the multitemporal cloud detection (MTCD) algorithm. This comparison reveals some improvements for cloud identification in complex landscapes, such as impervious surfaces and mixed areas of snow and cloud. Therefore, we argue that MTICZ provides an effective multitemporal method to detect cloud and cloud shadow for optical imagery in complicated landscapes.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Signal Processing and Radar Characteristics (SPARC) Simulator: A Flexible
           Dual-Polarization Weather-Radar Signal Simulation Framework Based on
           Preexisting Radar-Variable Data
    • Authors: David Schvartzman;Christopher D. Curtis;
      Pages: 135 - 150
      Abstract: This paper presents a novel, system-level, weather-radar time-series simulator able to ingest archived dual-polarization data and produce time-series data with the desired system and scanning parameters (e.g., antenna patterns, pulse repetition times, spatial sampling, waveform type). Time-series simulations are an important tool for testing signal processing techniques and can also be used to test the changes in system characteristics. The SPARC simulator ingests archived radar-variable data and produces dual-polarization time series with the desired system characteristics. First, the archived data are conditioned to fill in for missing or censored data. Then, based on the six meteorological variables, scattering centers are generated in a grid that matches the desired spatial sampling. For each scattering center, a spectrum shaping technique is used to create time-series data with the desired acquisition parameters. The effects of phase coding, pulse compression, range folding, waveform selection, and antenna patterns are incorporated in the data. In addition to conventionally sampled data, the simulator can produce range-oversampled data with the desired range correlation for range-time processing techniques. The results of applying diverse signal processing techniques and system designs on the simulated data show that the simulator can be used to qualitatively analyze the collective impact of a variety of those techniques on radar observables for any archived weather scenario.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • A Method for Surface Water Body Detection and DEM Generation With
           Multigeometry TanDEM-X Data
    • Authors: Yanan Du;Guangcai Feng;Zhiwei Li;Xing Peng;Zhengyong Ren;Jianjun Zhu;
      Pages: 151 - 161
      Abstract: This paper presents a novel method for surface water body detection and digital elevation model (DEM) generation simultaneously using multigeometry TanDEM-X pairs. The amplitude, coherence, and slope maps of the multigeometry data are used to detect water bodies. An iterative strategy by considering the perpendicular baseline and phase error is used to generate the fused DEM. The proposed method can overcome the disadvantages of the empirical threshold-based methods and the geometric distortion in the single-geometry synthetic aperture radar (SAR) water body detection. It can also improve the quality and accuracy of the fused DEM, especially in water areas and areas with geometric distortion. Five ascending and four descending TerraSAR-X/TanDEM-X pairs covering Zhuhai, China, were selected to validate the proposed method. Visual interpretation and quantitative analyses were applied to evaluate the accuracy of the detected water bodies and the fused DEM. The results show that: 1) more complete water body information is obtained compared with the single-geometry empirical threshold-based method; 2) the fused DEM refined by the detected water bodies displays good performance in separating water bodies from linear objects, e.g., bridges, and calculating the elevations of water areas; 3) the proposed method yields a high reliability and accuracy in surface water body detection, with a correctness of 90.2% and an overall accuracy of 92.0%; and 4) the accuracy of the fused DEM is about 3.4 m in a mountainous area and 10.2 m in an urban area with a high density of tall buildings.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Evaluation of Remotely Sensed Soil Moisture for Landslide Hazard
    • Authors: Lu Zhuo;Qiang Dai;Dawei Han;Ningsheng Chen;Binru Zhao;Matteo Berti;
      Pages: 162 - 173
      Abstract: Soil moisture is important in the triggering of many types of landslides. However, in situ soil moisture data are rarely available in hazardous zones. The advanced remote sensing technology could provide useful soil moisture information. In this study, an assessment has been carried out between the latest version of the European Space Agency Climate Change Initiative soil moisture product and the landslide events in a northern Italian region in the 14-year period 2002-2015. A clear correlation has been found between the satellite soil moisture and the landslide events, as over four-fifths of events had soil wetness conditions above the 50% regional soil moisture line. Attempts have also been made to explore the soil moisture thresholds for landslide occurrences under different environmental conditions (land cover, soil type and slope). The results showed slope distribution could provide a rather distinct separation of the soil moisture thresholds, with thresholds becoming smaller for steeper areas, indicating dryer soil condition could trigger landslides at hilly areas than in plain areas. The thresholds validation procedure is then carried out. Forty five rainfall events between 2014 and 2015 are used as test cases. Contingency tables, statistical indicators, and receiver operating characteristic analysis for thresholds under different exceedance probabilities (1%-50%) are explored. The results have shown that the thresholds using 30% exceedance probability provide the best performance with the hitting rate at 0.92 and the false alarm at 0.50. We expect this study can provide useful information for adopting the remotely sensed soil moisture in the landslide early warnings.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Disparity Refinement in Depth Discontinuity Using Robustly Matched
           Straight Lines for Digital Surface Model Generation
    • Authors: Rongjun Qin;Min Chen;Xu Huang;Kun Hu;
      Pages: 174 - 185
      Abstract: Dense image matching using remote sensing images is of particular importance for providing ground geometric data for object modeling and analysis. These methods normally operate in a rectified (epipolar) space producing disparity images that are correlated with the final three-dimensional geometry. However, the resulting digital surface models can be problematic on discontinuities of the terrain object, e.g., building edges, which largely limit their practical use for highly accurate object modeling. Existing works put forth efforts for dealing with this issue by defining better edge constraints and more global energy optimization, while intended to improve the disparity maps, it might be challenging to leverage the result of all the pixels through a single energy minimization, leading to either oversmoothed object boundaries or noisy surfaces. In this paper, we propose an intuitive method that integrates straight line primitives to enhance the disparity map. For each matched line, we perform a local discontinuity analysis and propose an intensity-based weighting method for a local plane fitting using iteratively solved weighted least squares adjustment, such that straightness of the object's edges (e.g., buildings) can be preserved, as the straight lines are detected and matched. Experiments on both aerial and satellite dataset show that the proposed method yields visually clear object edges and has achieved higher accuracy (2-3 pixels improvement) around edges than DSM generated from typical stereo matching result (i.e., from semiglobal matching).
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Integrated Assessment of Ground Surface Displacements at the Ketzin Pilot
           Site for CO2 Storage by Satellite-Based Measurements and Hydromechanical
    • Authors: Christin Lubitz;Thomas Kempka;Mahdi Motagh;
      Pages: 186 - 199
      Abstract: There has been growing interests in recent years for the safe underground storage of carbon dioxide (CO2) as a potential technology for preventing this greenhouse gas from entering the atmosphere. As suitable locations for geological storage may be diverse, the applicability of various geodetic and geophysical methods for surveillance and monitoring purposes must be investigated. In this paper, we evaluate the ground surface displacement at the Ketzin pilot site for CO2 storage in Germany, using satellite-based measurements and hydromechanical simulations. The InSAR observations, using more than four years of TerraSAR-X data from 2009 to 2013, reflect the stability of the Ketzin pilot site (long-term velocity
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Evaluation on Spaceborne Multispectral Images, Airborne Hyperspectral, and
           LiDAR Data for Extracting Spatial Distribution and Estimating Aboveground
           Biomass of Wetland Vegetation Suaeda salsa
    • Authors: Yingkun Du;Jing Wang;Zhengjun Liu;Haiying Yu;Zehui Li;Hang Cheng;
      Pages: 200 - 209
      Abstract: Suaeda salsa (S. salsa) has a significant protective effect on salt marshes in coastal wetlands. In this study, the abilities of airborne multispectral images, spaceborne hyperspectral images, and LiDAR data in spatial distribution extraction and aboveground biomass (AB) estimation of S. salsa were explored for mapping the spatial distribution of S. salsa AB. Results showed that the increasing spectral and structural features were conducive to improving the classification accuracy of wetland vegetation and the AB estimation accuracy of S. salsa. The fusion of hyperspectral and LiDAR data provided the highest accuracies for wetlands classification and AB estimation of S. salsa in the study. Multispectral images alone provided relatively high user's and producer's accuracies of S. salsa classification (87.04% and 88.28%, respectively). Compared to multispectral images, hyperspectral data with more spectral features slightly improved the Kappa coefficient and overall accuracy. The AB estimation reached a relatively reliable accuracy based only on hyperspectral data (R2 of 0.812, root-mean-square error of 0.295, estimation error of 24.56%, residual predictive deviation of 2.033, and the sums of squares ratio of 1.049). The addition of LiDAR data produced a limited improvement in the process of extraction and AB estimation of S. salsa. The spatial distribution of mapped S. salsa AB was consistent with the field survey results. This study provided an important reference for the effective information extraction and AB estimation of wetland vegetation S. salsa.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • A Hierarchical Multiscale Super-Pixel-Based Classification Method for
           Extracting Urban Impervious Surface Using Deep Residual Network From
           WorldView-2 and LiDAR Data
    • Authors: Mengfan Wu;Xiangwei Zhao;Zhongchang Sun;Huadong Guo;
      Pages: 210 - 222
      Abstract: High-resolution optical imagery can provide detailed information of urban land objects for impervious surface extraction, while airborne light detection and ranging (LiDAR) data can provide height features of land objects. Therefore, synergistic use of high-resolution imagery and LiDAR data is considered as an effective method to improve impervious surfaces extraction. In this paper, a novel hierarchical multiscale super-pixel-based classification method is proposed and applied to the urban impervious surfaces extraction from WorldView-2 and normalized digital surface model (nDSM) images derived from airborne LiDAR data. Three subsets in rural, rural–urban, and urban subsets are selected as the study areas. First, we split nonground and ground objects based on nDSM thresholds. Second, a hierarchical multiresolution segmentation method is used to generate nonground and ground super pixels. Then, we determine the multiscale input images based on the size of super pixels. Third, we construct optimal deep residual network (ResNet) and Spatial Pyramid Pooling (SPP-net) to train the model using multiscale input images. Finally, we use our deep models to predict hierarchically total super pixels in three subsets and generate the classification and impervious surfaces results. Our proposed method adopts hierarchical classification based on LiDAR nDSM height, which significantly improves the impervious surfaces extraction accuracies. Then, the deep residual network is applied further on multispectral and height fused data to extract urban impervious surfaces. Moreover, we propose an adaptive method to determine multiscale input images based on the segmentation of super pixels, which are inputs into the ResNet+SPP-net to train the deep model. Our proposed method reduces the uncertainty of multiscale input images and extracts better multiscale features. The results of the experiment show that our proposed method has a significant superiority to traditional -ixel-based method and single scale method for urban impervious surfaces extraction.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Synergistic Use of Optical and Dual-Polarized SAR Data With Multiple
           Kernel Learning for Urban Impervious Surface Mapping
    • Authors: Genyun Sun;Yanan Kong;Xiuping Jia;Aizhu Zhang;Jun Rong;Hongzhang Ma;
      Pages: 223 - 236
      Abstract: Accurate mapping of impervious surface distribution is important but challenging. Integrating optical and SAR data to improve urban impervious surface estimation has recently shown promising performance. Further investigation and development on this multisensory approach are conducted in this study. A novel multiple kernel learning (MKL) framework is proposed to integrate heterogeneous features from Landsat-8 and Sentinel-1A data effectively. A linearly weighted combination of basic kernels built using each group of features is learned as the optimal kernel, while the hyperparameters and the weight of each basic kernel are determined simultaneously by using the differential evolution algorithm. Then, the optimal kernel is embedded into the support vector regression algorithm, and the impervious surface abundance of the study area is estimated by applying the developed multiple kernel support vector regression (MKSVR) model. The impervious surface ground truth at a subpixel level is derived from a high-resolution image by means of object-oriented classification. The experimental results indicate that the synergistic use of optical and dual-pol SAR data by employing MKSVR achieves a noteworthy improvement for impervious surface estimation compared to that using optical image alone, the root mean square error is decreased by 4.30%, and the coefficient of determination (R2) is increased by 9.47%, and that the incorporation of optical and SAR does not guarantee the improved performance, simply stacking all features of multisource data into a vector is not a good choice, and the MKL is a powerful tool to apply as demonstrated by the experiments conducted in this study.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Boundary Delineation of Agricultural Fields in Multitemporal Satellite
    • Authors: Heather C. North;David Pairman;Stella E. Belliss;
      Pages: 237 - 251
      Abstract: Agricultural land-use statistics are more informative per-field than per-pixel. Land-use classification requires up-to-date field boundary maps potentially covering large areas containing thousands of farms. This kind of map is usually difficult to obtain. We have developed a new, automated method for deriving closed polygons around fields from time-series satellite imagery. We have been using this method operationally in New Zealand to map whole districts using imagery from several satellite sensors, with little need to vary parameters. Our method looks for boundaries-either step edges or linear features-surrounding regions of low variability throughout the time series. Local standard deviations from all image dates are combined, and the result is convolved with a series of extended directional edge filters. We propose that edge linearity over a long distance is a more important criterion than spectral difference for separating fields, so edge responses are thresholded primarily by length rather than strength. The resulting raster edge map (combined from all directions) is converted to vector (GIS) format and the final polygon topology is built. The method successfully segments parcels containing different crops and pasture, as well as those separated by boundaries such as roads and hedgerows. Here we describe the technique and demonstrate it for an agricultural study site (4000 km2) using SPOT satellite imagery. We show that our result compares favorably with that from existing segmentation methods in terms of both quantitative quality metrics and suitability for land-use classification.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Hybrid Model for Forecasting of Changes in Land Use and Land Cover Using
           Satellite Techniques
    • Authors: Adriana Mercedes Márquez;Edilberto Guevara;Demetrio Rey;
      Pages: 252 - 273
      Abstract: This paper proposes a hybrid model identified as krigging ordinary-forecasting models that contributes to predict the spatio-temporal of land use and land cover (LULC) changes using a unique predictor variable represented by the surface reflectance derived of satellite images, transformed in the principal component 1 (PC1). The tools used allow knowing the trends of spatial and temporal prediction models of PC1 semivariances and to judge the adjustment between observed and predicted variables by analyzing prediction statistics as: root-mean-squared error, mean absolute error, mean absolute percentage error, mean error, and mean percentage error. From the observation of statistics, the best spatio-temporal adjustment can be selected. The prediction of LULC changes through the PC1 prediction can be followed for different future time into the time series. The samples evaluated of PC1 prediction in the validation stage give a correlation coefficient upper to 0.8 and adjusted determination coefficient upper to 0.7; being a successful adjustment between observed and predicted values allowing to select the hybrid model proposed to forecast the PC1 variable in a future time. Likewise, an extensive time series is not required to get a good prediction, which has been obtained as a result of the test of three annual time series in different period constituted by a minimum of five years (2014-2018) and a maximum of eight years (1991-2003).
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Multifrequency Polarimetric SAR Image Despeckling by Iterative Nonlocal
           Means Based on a Space-Frequency Information Joint Covariance Matrix
    • Authors: Xiaoshuang Ma;Penghai Wu;Huanfeng Shen;
      Pages: 274 - 284
      Abstract: This paper presents an iterative nonlocal means (NLM) filtering method under the Bayesian framework to deal with the issue of multifrequency fully polarimetric synthetic aperture radar (PolSAR) image despeckling. Differing from most of the PolSAR filters designed for single-frequency data, the proposed NLM method is developed based on a space-frequency information joint covariance matrix, which can not only utilize multifrequency polarimetric information but also exploit the correlation between any two pixels in an image patch. Furthermore, with the aim of accelerating the filtering procedure and better retaining image details, an effective preselection step is employed. The filtering results obtained with both a simulated dataset and real multifrequency PolSAR datasets acquired by the AIRSAR system confirm the good performance of the proposed method in both reducing speckle and retaining details, when compared with some of the state-of-the-art despeckling algorithms.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Extended Geometrical Perturbation Based Detectors for PolSAR Image Target
           Detection in Heterogeneously Patched Regions
    • Authors: Dongwen Yang;Lan Du;Hongwei Liu;Yan Wang;Mingfei Gu;
      Pages: 285 - 301
      Abstract: Target detection in synthetic aperture radar image utilizing polarimetric information has attracted considerable attention. Single-target detector (STD), partial-target detector (PTD), and geometrical perturbation-polarimetric notch filter (GP-PNF) are three traditional polarimetric detectors based on polarimetric information. STD aims at detecting single targets, whereas PTD is suitable for partial targets. GP-PNF focuses on detecting targets with features, which are different from the homogeneous background. Both STD and PTD need a prior knowledge of the target, whereas GP-PNF needs to estimate the local clutter automatically. All these three methods use a feature vector to describe the character of the target or clutter. In fact, the feature vectors of the clutter and target may distribute in a subspace. Especially for the heterogeneous background, a feature vector cannot accurately describe the clutter. Motivated by this, this paper extends the clutter model from a complex feature vector to a complex feature subspace, which is suitable for a heterogeneously patched region and derives extended PTD and extended GP-PNF. Experimental results show the extended detectors’ validation and superiority to traditional detectors for target detection in heterogeneous regions.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • An Online Multiview Learning Algorithm for PolSAR Data Real-Time
    • Authors: Xiangli Nie;Shuguang Ding;Xiayuan Huang;Hong Qiao;Bo Zhang;Zhong-Ping Jiang;
      Pages: 302 - 320
      Abstract: Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and usually large scale. Fast and accurate classification is particularly important for their applications. By introducing online learning, the PolSAR system can learn a classification model incrementally from a stream of instances, which is of high efficiency for newly arrived samples processing, strong adaptability for a dynamically changing environment, and excellent scalability for rapidly increasing data. In this paper, we propose an Online Multi-view Passive-Aggressive learning algorithm, named OMPA, for PolSAR data real-time classification. The polarimetric, color, and texture features are extracted to characterize PolSAR data, and each type of features corresponds to one view. In order to exploit the consistency and complementary property of these views, we give a new optimization model that ensembles the classifiers of multiple distinct views and enforces the agreement between each predictor and the combined predictor. The corresponding algorithms for both binary and multiclass classification tasks are derived, and the update steps have analytical solutions. In addition, we rigorously derive a bound on the number of prediction mistakes of the method. The proposed OMPA algorithm is evaluated on two real PolSAR datasets for built-up areas extraction and land cover classification, respectively. Experimental results demonstrate that OMPA consistently maintains a smaller mistake rate with low time cost and achieves about 1% and 2% accuracy improvements on the datasets, respectively, compared with the best results of the previously known online single-view and multiview learning methods.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • A Generative Discriminatory Classified Network for Change Detection in
           Multispectral Imagery
    • Authors: Maoguo Gong;Yuelei Yang;Tao Zhan;Xudong Niu;Shuwei Li;
      Pages: 321 - 333
      Abstract: Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique
           for Remote Sensing Image
    • Authors: Peng Wang;Mauro Dalla Mura;Jocelyn Chanussot;Gong Zhang;
      Pages: 334 - 344
      Abstract: Super-resolution mapping (SRM) technique can explore the spatial distribution information of land cover classes in mixed pixels for multispectral image (MSI) or hyspectral image (HSI). Soft-then-hard super-resolution mapping (STHSRM) is an important type of SRM technique. STHSRM first utilizes the subpixel sharpening to produce the high-resolution fractional images with the soft attribute values for each subpixel and then allocates the hard class labels to each subpixel. However, due to the low resolution in the original image, the fractional images are difficult to pick up the full spatial-spectral information from the original image. In this paper, pansharpening technique is utilized in STHSRM (STHSRM-PAN) to produce the fractional images with more spatial-spectral information, which improves the mapping results. First, the original low-resolution MSI or HSI and a panchromatic image (PAN) are fused by pansharpening technique to produce the improved resolution image with the high spectral resolution of MSI or HSI and the high spatial resolution of PAN. The high-resolution fractional images with more spatial-spectral information are then obtained by unmixing the improved resolution image. Finally, the class labels are assigned to each subpixel according to the soft attribute values from the high-resolution fractional images. Comparing with the state-of-the-art STHSRM algorithms, the STHSRM-PAN shows the best performance with the percentage correctly classified and Kappa coefficient (Kappa) in the three experimental results.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • High Efficient Deep Feature Extraction and Classification of
           Spectral-Spatial Hyperspectral Image Using Cross Domain Convolutional
           Neural Networks
    • Authors: Yanhui Guo;Han Cao;Jianjun Bai;Yu Bai;
      Pages: 345 - 356
      Abstract: Recently, numerous remote sensing applications highly depend on the hyperspectral image (HSI). HSI classification, as a fundamental issue, has attracted increasing attention and become a hot topic in the remote sensing community. We implemented a regularized convolutional neural network (CNN), which adopted dropout and regularization strategies to address the overfitting problem of limited training samples. Although many kinds of the literature have confirmed that it is an effective way for HSI classification to integrate spectrum with spatial context, the scaling issue is not fully exploited. In this paper, we propose a high efficient deep feature extraction and the classification method for the spectral-spatial HSI, which can make full use of multiscale spatial feature obtained by guided filter. The proposed approach is the first attempt to lean a CNN for spectral and multiscale spatial features. Compared to its counterparts, experimental results show that the proposed method can achieve 3% improvement in accuracy, according to various datasets such as Indian Pines, Pavia University, and Salinas.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Superpixel-Based Semisupervised Active Learning for Hyperspectral Image
    • Authors: Chenying Liu;Jun Li;Lin He;
      Pages: 357 - 370
      Abstract: In this work, we propose a new semisupervised active learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via semisupervised learning. The learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based semisupervised learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for semisupervised learning in hyperspectral image classification.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Implementation of Hybrid Ionospheric TEC Forecasting Algorithm Using
           PCA-NN Method
    • Authors: I. Lakshmi Mallika;D. Venkata Ratnam;Yuichi Ostuka;G. Sivavaraprasad;Saravana Raman;
      Pages: 371 - 381
      Abstract: Forecasting the ionospheric space weather is crucial for improving the accuracy of the global navigation satellite systems (GNSS). Nonetheless, comprehending the nonhomogeneous ionospheric variability under space earth environmental conditions is a major challenge, and so is developing an accurate ionospheric forecasting model. The complex spatial and temporal variations in the ionospheric region are the results of the solar and interplanetary activities, in addition to the magnetosphere, mesosphere, thermosphere, stratosphere, troposphere, and lithosphere processes. Thus, this calls for an urgent need to develop a suitable ionospheric forecasting algorithm to capture the ionospheric perturbations. Total electron content (TEC) is the key parameter derived from GNSS receivers to represent the status of the ionosphere. This paper introduces a novel ionospheric forecasting algorithm based on the fusion of principal component analysis and artificial neural networks (PCA-NN) methods to forecast the ionospheric TEC values. Solar index (F10.7), geomagnetic index (Ap index), and 20-year TEC data (1997-2016) over a Japan Grid Point (34.95 °N and 134.05 °E) were used to apply artificial intelligence methodologies. The experimental results underscore the reliability of the proposed algorithm in forecasting the ionospheric time delay effects.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
  • Enhancing 3-D Seismic Data Using the t-SVD and Optimal Shrinkage of
           Singular Value
    • Authors: Rasoul Anvari;Mokhtar Mohammadi;Amin Roshandel Kahoo;
      Pages: 382 - 388
      Abstract: We consider the three-dimensional (3-D) seismic data as a tensor data of size n1 × n2 × n3 contaminated with the white Gaussian noise. A new version of the tensor robust principal component analysis (TRPCA) is employed for denoising the 3-D seismic data. In the new TRPCA, the singular values are extracted using the optimal shrinkage method. We recover the low-rank matrices from noisy data by shrinkage of the singular values, in which the singular value thresholding in the Fourier domain is exploited to extract the low-rank component of the tensor. The algorithm is as follows. First, by assuming the incoherency conditions the whole tensor is modeled as a combination of a low-rank component and a sparse component. Second, the Fourier transform of the tensor is computed along the third dimension of the tensor, then the singular value decomposition (SVD) is computed in the Fourier domain, then the low-rank component is extracted by shrinkage of the singular values. Finally, the steps mentioned above are repeated until the Frobenius norm of the error matrix reaches the desired value. We evaluate the performance of the proposed method, which is called tensor optimal shrinkage of SVD based on the qualitative and quantitative measurements, and compare it with state-of-the-art methods such as iterative tensor singular value thresholding and 4-D block matching using different types of synthetic and real seismic data.
      PubDate: Jan. 2019
      Issue No: Vol. 12, No. 1 (2019)
School of Mathematical and Computer Sciences
Heriot-Watt University
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