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  Subjects -> ELECTRONICS (Total: 154 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Microelectronic Engineering     Open Access   (Followers: 11)
Advances in Power Electronics     Open Access   (Followers: 23)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 202)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 23)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 12)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
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  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 40)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 13)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 161)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 72)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 56)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 60)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 40)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 3)
Energy Storage Materials     Full-text available via subscription   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 5)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 127)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 54)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 44)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 31)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 48)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 44)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 48)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 15)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 31)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 13)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 22)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 54)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 7)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 15)
IET Power Electronics     Hybrid Journal   (Followers: 29)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 17)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 11)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 33)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 16)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 7)
International Journal of Antennas and Propagation     Open Access   (Followers: 10)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 3)
International Journal of Electronics & Data Communication     Open Access   (Followers: 10)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 12)
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: 9)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 14)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 7)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 5)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 8)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 8)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 16)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 5)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 125)
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: 5)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 9)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 2)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 31)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 9)
Journal of Semiconductors     Full-text available via subscription   (Followers: 3)
Journal of Sensors     Open Access   (Followers: 21)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 14)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 32)
Nanotechnology, Science and Applications     Open Access   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Journal of Antennas and Propagation     Open Access   (Followers: 5)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 13)
Paladyn, Journal of Behavioral Robotics     Open Access  
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 Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 9)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 4)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 48)
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: 5)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 57)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 7)
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  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 6)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 12)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [48 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • [Front cover]
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Institutional Listings
    • Abstract: Advertisements.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Validation of MODIS Sea Surface Temperature Product in the Coastal Waters
           of the Yellow Sea
    • Authors: Yanling Hao;Tingwei Cui;Vijay P. Singh;Jie Zhang;Ruihong Yu;Zhilei Zhang;
      Pages: 1667 - 1680
      Abstract: Sea surface temperature (SST) plays a fundamental role in the exchange of heat, momentum, and water vapor between atmosphere and ocean. Therefore, measurement of SST has been done from ships, buoys, offshore platforms, and satellites. During past decades satellites are being increasingly used, because datasets over wide areas can be obtained. In this paper, moderate resolution imaging spectroradiometer (MODIS) on board Terra and Aqua SST products were examined and validated for coastal waters in the Yellow Sea by using the in situ buoy data. A strict match-up method was adopted in view of the complexity and variability of coastal area, resulting in 154 and 164 match-ups for Terra and Aqua, respectively. The MODIS SST agreed well with in situ buoy SST, with squared correlation coefficients R2 of 0.989 for Terra, and 0.987 for Aqua. Relative to in situ SSTs, the satellite-derived SSTs had a bias of 0.23 °C and 0.06 °C, a standard deviation of 0.79 °C and 0.85 °C, and a root mean square error of 0.83 °C and 0.85 °C, for Terra and Aqua. The differences between MODIS and in situ SST exhibited apparent seasonal variations. The accuracy of MODIS SST products for spring and summer were lower than those for autumn and winter, which could be caused by frequent sea fog along the southern coast of Shandong Peninsula. The SST bias approximately depended on wind velocity; low wind velocity could enhance the diurnal SST amplitude and the bulk-skin temperature difference. However, there is no significant dependence of the bias on air-sea temperature difference or surface flow velocity.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Satellite-Based Nowcasting of Extreme Rainfall Events Over Western
           Himalayan Region
    • Authors: Bipasha Paul Shukla;C. M. Kishtawal;Pradip K. Pal;
      Pages: 1681 - 1686
      Abstract: Western Himalayan (WH) region is considered to be one of the most vulnerable spots for flash flooding-related natural disasters in the world. The confluence of moist air advected from Arabian sea with complex terrain has produced a series of extreme rainfall triggered disasters in recent past. However, the causal events leading to these enhanced episodes of precipitation lack clarity and thus flash flood forecasting still remains a big challenge. In this paper, we address the problem by studying cloud development over this region and its relationship with the underlying topography. Our results demonstrate that WH region is mostly inhabited by low-medium level clouds and governed by warm rain processes. Satellite-based analysis shows that in comparison to cloud top temperature, cloud top cooling rate (CTCR) is a better indicator for extreme rain producing events over this region. A model for nowcasting of extreme orographic rain events has thus been developed using the spatial characteristics of CTCR to predict potential locations for orographically induced severe precipitation events. The heavy rainfall nowcasts produced by this methodology, when compared with global precipitation fields, show very encouraging results. The probability of positive identification of a heavy rainfall event is 82.8%, with a false alarm rate of 29.7% and average lead time of 2-3 h. The insights provided by this study will give an impetus to the flash flood advance warning over WH region bringing about a significant beneficial societal impact.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Mapping Thermokarst Lakes on the Qinghai–Tibet Plateau Using Nonlocal
           Active Contours in Chinese GaoFen-2 Multispectral Imagery
    • Authors: Bangsen Tian;Zhen Li;Meimei Zhang;Lei Huang;Yubao Qiu;Zhixian Li;Panpan Tang;
      Pages: 1687 - 1700
      Abstract: In order to monitor the response of thermokarst lakes on the Qinghai-Tibet Plateau (QTP) to rapid climatic changes and human activities, an automated method for extracting shorelines from Chinese GaoFen-2 (GF-2) imagery is proposed. First, the water index (WI) images and the potential lake areas are calculated from the preprocessed multispectral imagery and digital elevation model data, respectively. Second, the initial segmentation obtained by global thresholding of the WI images and masking in the potential lake areas are used to implement the contour initialization of active contours models efficiently. Finally, the nonlocal active contours (NLAC) approach is applied to refine the initial segmentation of the WI images, and the final shoreline vector files are produced by some simple and automatic postprocessing steps. Experiments on the GF-2 imagery demonstrate that 1) by exploiting the capability of WI to locate the approximate shoreline effectively around the evolving contour, the processing time of the proposed method can be saved significantly; 2) the NLAC approach can efficiently identify the shoreline by integrating the nonlocal interactions between pairs of patches inside and outside the lake; and 3) the proposed method can conveniently adapt to the multitemporal and multifeature image analysis. Using the manual digitized shorelines as the reference data, an average error of less than one pixel with standard deviation of 0.1320 can be obtained. These results prove that the proposed method is feasible for the identification and monitoring of thermokarst lakes on the QTP.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Estimate of Ocean Wind Vectors Inside Tropical Cyclones From Polarimetric
    • Authors: Xiaobin Yin;Zhenzhan Wang;Qingtao Song;Yingzhu Huang;Ruanyu Zhang;
      Pages: 1701 - 1714
      Abstract: Microwave polarimetry provides a distinctive opportunity to retrieve wind speed (WS) and wind direction (WD) inside tropical cyclones (TCs) due to the high atmospheric transmissivity through clouds and under rain conditions. A WS retrieval algorithm for WS above 20 m/s in TCs using brightness temperature at 6.8- and 10.7-GHz has been developed and a new set of parameters has been optimized from WindSat TB and the HWind analysis matches. This algorithm is estimated to have an encouraging degree of accuracy for retrieving WS in TCs. For WS above 20 m/s, the mean (std) of the differences between retrieved WS and HWind analysis for 17 TCs from 2003 to 2009 is 0.2 m/s (3.1 m/s). WD signals in the third (T3) and fourth Stokes (T4) parameters at 10.7-, 18.7- and 37-GHz for ocean surfaces in TCs under rain are presented. T3 observations from the WindSat 10.7-, 18.7-, and 37-GHz channels are collocated with the ocean-surface winds from the HWind analysis. The collocated data are binned as a function of WS and WD. The 10.7 GHz T3 data show clear 4-K peak-to-peak directional signals at 30-40 m/s WS after correction for the atmospheric attenuation. The data are fitted by harmonics of the relative azimuth angles between the HWind analysis and radiometer look directions. The new coefficients of WD harmonics are used to retrieve WD in TCs under rain using WindSat T3 and T4 channels. The rms difference between retrieved WD and HWind WD is 24.2.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • The Evolution of Springtime Water Vapor Over Beijing Observed by a High
           Dynamic Raman Lidar System: Case Studies
    • Authors: Tianning Su;Jian Li;Jing Li;Chengcai Li;Yiqi Chu;Yiming Zhao;Jianping Guo;Yong Yu;Lidong Wang;
      Pages: 1715 - 1726
      Abstract: Raman lidar is an effective technique to retrieve the vertical distribution of atmospheric water vapor. For the first time, we present water vapor profiles retrieved by a high dynamic Raman lidar system over the Beijing area for representative cases in spring 2014, within the framework of the Aerosol Multi-wavelength Polarization Lidar Experiment project. In springtime, water vapor content over Beijing is generally low but with a strong daily variability. Its evolution is strongly coupled with winds and aerosols, with clouds also exerting a distinct impact. Northwesterly winds is found to be the most important factor impacting the temporal variability of water vapor mixing ratio (WVMR), and WVMR is found to be negatively correlated with wind speed. Moreover, we find that clouds tend to cause significant increases in the standard deviation of WMVR measurement, and relative humidity sharply increase below the cloud base. During a typical pollution episode, water vapor strongly covaries with aerosols due to hygroscopic growth effect and transport mechanism. Both water vapor and aerosols exhibit the highest variability within the planetary boundary layer (PBL), where the development and dissipation of haze mainly occur. Within the PBL, water vapor and aerosol concentration demonstrate different evolution features at different altitudes during the haze process, with a delayed increase and early decrease for higher altitudes. Back trajectory analysis using the hybrid single-particle Lagrangian trajectory model indicates that this phenomenon is most likely associated with different sources of the air mass at different altitudes.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Comparing Performances of Crop Height Inversion Schemes From
           Multifrequency Pol-InSAR Data
    • Authors: Manuele Pichierri;Irena Hajnsek;
      Pages: 1727 - 1741
      Abstract: Polarimetric synthetic aperture radar (SAR) interferometry has shown great potential to estimate the height of crops and forests by inverting simple scattering models of the canopy and the underlying soil. The random-volume-over-ground (RVoG) model assumes that the scatterers within the canopy (e.g., stalks and leaves) are not aligned along a preferred direction. If these scatterers are characterized by a correlation of orientations, then the scene is better described by the oriented-volume-over-ground (OVoG) model. This paper investigates the plausibility of the “random volume” and “oriented volume” assumptions, as well as the robustness of single- and dual-baseline inversion schemes in relation to agricultural crop height estimation. To this end, we implemented different single- and dual-baseline techniques for the inversion of the RVoG and OVoG models, and we evaluated their height retrieval performances with the help of simulated observations and experimental F-SAR measurements in L-, C-, and X-Bands. The inversion results revealed a positive relationship between the bias of the estimated height and the differential extinction when the RVoG inversion scheme is applied. By contrast, no such dependence was observed for the OVoG inversion, whose height estimates are on average consistent with the actual values (i.e., median bias below 10% in magnitude). Despite the observed superiority of dual-baseline approaches, the study also pointed out the feasibility of crop height estimation using single-baseline RVoG inversion schemes, provided the appropriate a priori constraints (e.g., on the extinction coefficient) and crop-specific configuration parameters (e.g., C-Band for maize, and C- and X-Bands for wheat).
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Evidential Fusion Based Technique for Detecting Landslide Barrier Lakes
           From Cloud-Covered Remote Sensing Images
    • Authors: Xi Chen;Jing Li;Yunfei Zhang;Weiguo Jiang;Liangliang Tao;Wei Shen;
      Pages: 1742 - 1757
      Abstract: Landslide barrier lakes usually form quickly after disasters and require very timely remote sensing images to monitor the land-cover change. However, cloud-free images are not always available in emergency situations. This paper provides a method to fuse multitemporal cloud-covered images for change detection, based on the evidential fusion framework. First, the frame of discernment is defined by postclassification comparison results. Second, a way of measuring the basic belief assignment (BBA) is introduced based on the confusion matrixes. Next, a simple BBA redistribution process is proposed to deal with cloud coverage problems. Then, the complementary and redundant information from the input images can be fused following the evidence combination and decision making rules in the evidential fusion framework. Finally, the land-cover change map can be derived. Thanks to the Dempster-Shafer evidence theory, the proposed method can complete the change detection process-including data fusion and cloud removal-in an integrated manner. The proposed method is applied to detect the landslide barrier lake in a real case study, using a series of cloud-covered images from the GF-1 satellite. Result comparisons show that the proposed method is more effective than some basic fusion strategies that perform change detection and cloud removal in separate steps. Then, some approaches to improve the proposed method are discussed: introducing new evidence combination rule, improving the classification accuracy, and adding new evidences. All the results indicate the potential of evidential fusion for change detection from cloud-covered images.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Postearthquake Landslides Mapping From Landsat-8 Data for the 2015 Nepal
           Earthquake Using a Pixel-Based Change Detection Method
    • Authors: Wei Zhao;Ainong Li;Xi Nan;Zhengjian Zhang;Guangbin Lei;
      Pages: 1758 - 1768
      Abstract: The 2015 Nepal earthquake and its aftershocks not only caused huge damage with severe loss of life and property, also induced many geohazards with the major type of landslide which should bring continuous threats to the affected region. To gain a better understanding of the landslides induced by this earthquake, we proposed a pixel-based change detection method for postearthquake landslide mapping by using bitemporal Landsat-8 remote sensing data [May 29, 2014 (pre-earthquake) and June 1, 2015 (postearthquake)]. Two river valleys (Trishuli river valley and Sun Koshi river valley) that contain important economic arteries linking Nepal and China were selected as the study areas. Validation of the mapping results with postearthquake high-resolution images from Google Earth shows that the pixel-based landslide mapping method is able to identify landslides with relatively high accuracy, and it also approves the applicability of Landsat-8 satellite for landslide mapping with its multispectral information. The spatial distribution analysis indicates that both river valleys are substantially affected by landslides, and the situation is even more serious in the high mountain areas. Landslides are generally found in areas of high elevation and large surface slopes, with mean values above 1600 m and 30°, respectively. These findings suggest that these areas suffer greatly from these geohazards, and the threat will continue for the next few years.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Spatiotemporal Fuzzy Clustering Strategy for Urban Expansion Monitoring
           Based on Time Series of Pixel-Level Optical and SAR Images
    • Authors: Shuang Li;Yafei Wang;Peipei Chen;Xinliang Xu;Chengqi Cheng;Bo Chen;
      Pages: 1769 - 1779
      Abstract: Monitoring urban expansion dynamically using remote sensing technology is an essential method for obtaining and understanding urban spatial structure. However, the quality of traditional optical images in some areas is poor due to clouds and fog. Compared to optical images, synthetic aperture radar (SAR) can achieve earth observations without the limits of sunlight and weather conditions, but its speckle is too obvious. This paper combined the advantages of pixel-level optical image and SAR image time series and proposed a spatiotemporal fuzzy clustering (STFC) strategy for urban expansion monitoring. This strategy includes three parts: 1) the construction of optical-SAR image mixed time series; 2) a time-series fuzzy information granulation method to ascertain change nodes; and 3) STFC to determine the change types and range. In our study, 13 TM images and 25 SAR scenes taken from 2005 to 2011 were selected as raw data. We used the proposed method to monitor the urban expansion of Chengdu, China, and then, analyzed its main causes according to the monitoring results. The results suggested that: 1) the proposed methods could effectively extract the change nodes and change pixels, with the correctness of 85.20% and the completeness of 86.06%, outperforming the time series only (nonspatial) fuzzy clustering method, as well as traditional classification methods; and 2) the urban expansion of Chengdu is most apparent from 2005 to 2011, with the expansion direction shifting from the traditional ring structure expansion to point-axis expansion following the priority given to construction of new urban areas.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Object-Based Analysis and Fusion of Optical and SAR Satellite Data for
           Dwelling Detection in Refugee Camps
    • Authors: Kristin Spröhnle;Eva-Maria Fuchs;Patrick Aravena Pelizari;
      Pages: 1780 - 1791
      Abstract: This study investigates the potential of very high spatial resolution (VHSR) optical WorldView-2 (WV-2) and single-polarized TerraSAR-X (TSX) synthetic aperture radar (SAR) satellite data for an automated detection of different dwelling types in a refugee camp by means of object-based image analysis (OBIA). First, the optical data and SAR data are analyzed independently, and then a fusion of both data sets is performed applying two different approaches: 1) an overlay operation-based procedure integrating the independent results of the optical- and SAR-based dwelling detection, and 2) a feature-based analysis approach taking advantage of the conjoint analysis of both data sets. The results of the single-sensor and the data fusion approaches are evaluated in detail on the basis of object-based and area-based accuracy assessments. Advantages and limitations of the analysis approaches are discussed. The accuracy rates reveal that the use of optical satellite data shows promising results regardless of the dwelling material, while the SAR data are suitable for the detection of metal sheet dwellings only. In complex camp areas, with closely spaced containers, the results of the independent analyses can be improved significantly by the proposed fusion approaches. The combination of SAR and optical data allows for the separation of contiguous dwellings in cases this was not possible by the optical sensor information.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Novel Fast Coherent Detection Algorithm for Radar Maneuvering Target With
           Jerk Motion
    • Authors: Jiancheng Zhang;Tao Su;Jibin Zheng;Xuehui He;
      Pages: 1792 - 1803
      Abstract: The detection performance of radar maneuvering target with jerk motion is affected by the range migration (RM) and Doppler frequency migration (DFM). To address these problems, a fast algorithm without searching target's motion parameters is proposed. In this algorithm, the second-order keystone transform is first applied to eliminate the quadratic coupling between the range frequency and slow time. Then, by employing a new defined symmetric autocorrelation function, scaled Fourier transform, and inverse fast Fourier transform, the target's initial range and velocity are estimated. With these two estimates, the azimuth echoes along the target's trajectory, which can be modeled as a cubic phase signal (CPS), are extracted. Thereafter, the target's radial acceleration and jerk are estimated by approaches for parameters estimation of the CPS. Finally, by constructing a compensation function, the RM and DFM are compensated simultaneously, followed by the coherent integration and target detection. Comparisons with other representative algorithms in computational cost, motion parameter estimation performance, and detection ability indicate that the proposed algorithm can achieve a good balance between the computational cost and detection ability. The simulation and raw data processing results demonstrate the effectiveness of the proposed algorithm.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Processing Sliding Mosaic Mode Data With Modified Full-Aperture Imaging
           Algorithm Integrating Scalloping Correction
    • Authors: Ning Li;Robert Wang;Yunkai Deng;Tuan Zhao;Wei Wang;Heng Zhang;
      Pages: 1804 - 1812
      Abstract: Modified full-aperture imaging algorithm for sliding Mosaic mode synthetic aperture radar (SAR) is presented in this paper, which includes scalloping correction and spikes suppression. The full-aperture imaging algorithm is introduced into Mosaic mode and validated by real C-band airborne SAR imaging experiments. The main idea is to substitute zeros between bursts with linear-predicted data extrapolated from adjacent bursts to suppress the spikes caused by multibursts processing. Furthermore, scalloping correction for sliding Mosaic mode is integrated with this algorithm. It is innovational to correct the azimuth beam pattern weighting altered by radar antenna rotation in azimuth with deramping preprocessing operation. Finally, experiments performed by the C-band airborne SAR system with a maximum bandwidth of 200 MHz validate the effectiveness of the approach.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Characterization of the Spatial Variability of In-Situ Soil Moisture
           Measurements for Upscaling at the Spatial Resolution of RADARSAT-2
    • Authors: Imen Gherboudj;Ramata Magagi;Aaron A. Berg;Brenda Toth;
      Pages: 1813 - 1823
      Abstract: This study characterizes the spatial variability of soil moisture measurements using statistical and geostatistical analyses for the transferability of the ground measurements to the scale of the spatial resolution of RADARSAT-2 images. It aims to better interpret synthetic aperture radar backscattering relationships to soil moisture. Coincident with RADARSAT-2 overpasses, soil and crop parameters were measured in July 2008 and August 2009 over two Canadian agricultural sites (Kenaston, Saskatchewan, and Lennoxville, Quέbec). The measured soil moisture was used to determine the theoretical semivariogram models that fit the experimental semivariograms. An inverse correlation is obtained between the soil moisture coefficient of variation (CV) and the range (spatial correlation) of the semivariogram, which can assess the degree of the spatial correlation between the samples of each field. Soil moisture measurements with high values of CV (20%-40%) are correlated within a distance less than 10 m and those with lower CV (10%-20%) are correlated within a larger distance varying between 12 and 46 m. The soil moisture measurements of each field were upscaled to the spatial resolution of RADARSAT-2 images (6 × 5 and 9 × 5m2) using either simple kriging or ordinary kriging. The results were cross validated using the surface scattering component, which is extracted from the Freeman-Durden decomposition applied to fully polarimetric RADARSAT-2 images. They show that the kriging-based soil moisture better represents RADARSAT-2 surface scattering with strong clustered linear regressions (R2 greater than '0.6, RMSE lower than '0.9 dB, and p-value of slope less than 0.05) than the nonkriged soil moisture samples.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • SAR Image Content Retrieval Based on Fuzzy Similarity and Relevance
    • Authors: Xu Tang;Licheng Jiao;William J. Emery;
      Pages: 1824 - 1842
      Abstract: This paper presents a new content-based synthetic aperture radar (SAR) image retrieval method to search out SAR image patches, which consists of two essential parts: an initial retrieval and later refined results. To obtain the proper initial retrievals, we develop a similarity measure named region-based fuzzy matching (RFM) to evaluate the similarities between SAR image patches. First, to reduce the negative influence of speckle noise, we segment the SAR image patches into brightness-texture regions at the superpixel level rather than the pixel level. Second, a multiscale edge detector is utilized to resolve the multiscale property of the SAR image patches, and then the edge regions of the SAR image patches are defined by those edge features. Third, to overcome the segmented uncertainty and the blurry boundaries, the obtained regions are described by fuzzy features. Finally, the RFM similarity between two SAR image patches is converted into the linear summation of the resemblance between different fuzzy feature sets. After we obtain the initial retrieval results, the multiple relevance feedback (MRF) scheme is proposed to refine the original ranked list. In this scheme, different relevance feedback approaches are carried out respectively, and then their results are fused to improve the initial retrieval. In addition, a new kernel function based on the RFM measure is developed for MRF. The encouraging experimental results counted on a manually constructed ground truth SAR image patch dataset demonstrate that our retrieval method is effective for SAR images compared with some existing approaches proposed in the remote sensing community.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Scattering Property Analysis of Supraglacial Debris Using Target
           Decomposition on Polarimetric SAR Imagery
    • Authors: Lei Huang;Bang-sen Tian;Zhen Li;Jian-min Zhou;
      Pages: 1843 - 1852
      Abstract: Supraglacial debris is widely distributed in the ablation zones of the glaciers in mountain valleys, and it influences glacier melting considerably. Polarimetric Synthetic Aperture Radar (SAR) presents promising results in terms of glacier classification and monitoring, but the scattering mechanism of debris has been unclear until now. In this paper, we attempted to verify the main scattering components of debris in the L- and C-bands polarimetric SAR images. A newly developed target decomposition method that is specially designed for debris is used to quantitatively analyze the scattering component. The method combines the X-Bragg surface scattering, double bounce, and completely random volume scattering models. The results from the target decomposition agree well with the scattering property analysis from the phase difference and entropy-alpha methods. The Keqikaer glacier, which is in the southern Tianshan Mountains, is selected as the study area. Phased-Array L-band SAR (PALSAR) images from the Advanced Land Observing Satellite (ALOS), PALSAR-2 images from ALOS-2, and RADARSAT-2 polarimetric SAR images are employed. The results show that in the C-band, surface scattering is dominant in debris, and it accounts for approximately 70% of the total power; in the L-band, volume scattering increases to a larger portion (approximately 40%), but remains slightly weaker than surface scattering (approximately 56%).
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Performance Analysis of TanDEM-X Quad-Polarization Products in Pursuit
           Monostatic Mode
    • Authors: José-Luis Bueso-Bello;Michele Martone;Pau Prats-Iraola;Carolina González-Chamorro;Thomas Kraus;Jens Reimann;Marc Jäger;Benjamin Bräutigam;Paola Rizzoli;Manfred Zink;
      Pages: 1853 - 1869
      Abstract: Since 2010, the two twin synthetic aperture radar (SAR) satellites TerraSAR-X and TanDEM-X have been acquiring high-resolution images to generate a global Earth's digital elevation model (DEM). Both satellites have been flying in a controlled close orbit formation, acquiring data in the nominal bistatic stripmap single-polarization mode. Once the acquisition of the dataset for the generation of the DEM has been completed, the flexibility offered by both SAR instruments in terms of interferometric, imaging, and polarization modes has been further exploited to demonstrate the different capabilities of the TanDEM-X experimental modes. By activating the dual-receive antenna mode, full polarimetric data can be acquired. For the first time, it has been possible to systematically command quad-polarization acquisitions in a dedicated TanDEM-X mission science phase, started in October 2014. In this paper, we present a first performance analysis and quality assessment of such quad-polarization products. The SAR image resolution and the noise equivalent sigma zero have been evaluated to show the quality of the focused SAR products. The influence of different instrument parameters on the SAR and interferometric performance, such as chirp bandwidth, pulse repetition frequency, or block adaptive quantization, has been investigated as well. For the evaluation of the interferometric performance, key parameters such as coherence and interferometric phase error have been analyzed. In this paper, the obtained results are presented and recommendations are given for the optimization in the commanding of TanDEM-X quad-polarization acquisitions.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Removal of Optically Thick Clouds From High-Resolution Satellite Imagery
           Using Dictionary Group Learning and Interdictionary Nonlocal Joint Sparse
    • Authors: Ying Li;Wenbo Li;Chunhua Shen;
      Pages: 1870 - 1882
      Abstract: In this paper, we propose a method for cloud removal in a cloud-contaminated high-resolution (HR) optical satellite image with two kinds of auxiliary images of different types: a low-resolution (LR) optical satellite composite image and a synthetic aperture radar (SAR) image. In the proposed method, we assume that cloud-contaminated and cloud-free regions have been detected accurately, then dictionary group learning (DGL) is used to establish structure correspondences between HR, LR, and SAR data from cloud-free patches, while interdictionary nonlocal joint sparse coding (INJSC) is used to estimate the universal representation coefficients of patches contaminated by clouds, and finally, cloud-contaminated HR patches can be reconstructed with their universal coefficients and the HR dictionary learned from DGL process. In this way, the missing information in the cloud-contaminated HR image can be reconstructed patch by patch. The proposed method is tested on a series of experiments on both simulated and real data. Experimental results show that both DGL and INJSC are beneficial to better reconstructing the missing information. This method is also compared against our previous work on the same topic, which adopted dictionary pair learning (DPL) and sparse coding (SC) to recover the missing information and achieved state-of-the-art performance at that time. The comparison shows that the method proposed in this paper significantly outperforms the previous one.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Spectral–Spatial Adaptive Area-to-Point Regression Kriging for MODIS
           Image Downscaling
    • Authors: Yihang Zhang;Peter M. Atkinson;Feng Ling;Qunming Wang;Xiaodong Li;Lingfei Shi;Yun Du;
      Pages: 1883 - 1896
      Abstract: The moderate resolution imaging spectroradiometer (MODIS) sensor contains 36 bands at spatial resolutions of 250 m (e.g., bands 1-2), 500 m (e.g., bands 3-7), and 1000 m (e.g., bands 8-36). The first seven bands covering the visible to midinfrared wavelengths have been used widely for monitoring the Earth's surface. However, 500 m MODIS bands 3-7 present challenges for use in land cover/land use applications, as many land cover features on the Earth's surface possess complex structures with a spatial resolution finer than 500 m. Fusing MODIS 250 m bands 1-2 and 500 m bands 3-7 is an attractive proposition, that is, increasing the spatial resolution of bands 3-7. The geostatistical based downscaling approach, area-to-point regression kriging (ATPRK), has shown great potential for MODIS image downscaling. However, it considers the global relationship between bands 1-2 and each of bands 3-7 to select a 250 m PAN-like band from bands 1-2, which may not take full advantage of both bands 1 and 2. In this paper, a new geostatistical downscaling method of spectral-spatial adaptive ATPRK (SSAATPRK) is proposed for MODIS image downscaling. Both fine spatial resolution bands (i.e., bands 1 and 2) are used as the input to SSAATPRK, and there is no need to choose a PAN-like band for each coarse band, as in the original ATPRK method. SSAATPRK was compared to four benchmark image fusion methods, including principal component analysis, high-pass filtering, ATPRK, and adaptive ATPRK (AATPRK), using one synthetic MODIS image experiment and two real MODIS image experiments. Both visual and quantitative evaluations demonstrated that SSAATPRK produced results consistently with the greatest amount of spatial detail and the largest accuracy. Furthermore, SSAATPRK inherits completely the advantages of ATPRK and AATPRK, while extending them for MODIS image downscaling.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Physically Based Model for Multispectral Image Simulation of Earth
           Observation Sensors
    • Authors: Xiaoyu He;Xiaojian Xu;
      Pages: 1897 - 1908
      Abstract: Physically based multispectral image simulation consists of sensor system modeling, bottom-of-atmosphere (BOA) image generation, and top-of-atmosphere (TOA) image calculation. TOA radiance images are usually generated using a lookup table (LUT) for computational efficiency, which is calculated by means of atmospheric radiative transfer codes with different combination of input variables, including viewing zenith, solar zenith, and relative azimuth angles; visibility; columnar water vapor; and ground elevation. In this paper, a new strategy is proposed for TOA radiance image simulation, where transmitted surface radiance and atmospheric radiance at the TOA are calculated, respectively, to improve accuracy as well as efficiency. The transmitted surface radiance image is obtained from pixel-by-pixel calculation of BOA radiance and path transmittance. In calculating the atmospheric radiance of TOA, two LUTs are built for the emitted and the scattered radiance from each atmospheric layer, respectively. The effects of visibility and columnar water vapor on the atmospheric radiance are characterized by means of an equivalent path transmittance, which is related to the scene geometry as well as the thickness of atmospheric layer. In this way, when a new scene is simulated, except for three variables, i.e., viewing and solar zenith angles and atmospheric layer number, other parameters are set as constants in building the LUTs, enabling more combinations of input variables without adding excessive computational burden. Multispectral images in different bands with moderate spatial resolution are simulated and compared with the moderate-resolution imaging spectroradiometer (MODIS) images to demonstrate the accuracy and the usefulness of the proposed strategy.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Occluded Object Detection in High-Resolution Remote Sensing Images Using
           Partial Configuration Object Model
    • Authors: Shaohua Qiu;Gongjian Wen;Yaxiang Fan;
      Pages: 1909 - 1925
      Abstract: Deformable-part-based model (DPM) has shown great success in object detection in recent years. However, its performance will degrade on partially occluded objects and is even worse on largely occluded objects in real remote sensing applications. To address this problem, a novel partial configuration object model (PCM) is developed in this paper. Compared to conventional single-layer DPMs, an extra partial configuration layer, which is composed of partial configurations defined according to possible occlusion patterns, is introduced in PCM to block the transmission of occlusion impact. During detection, each hypothesis from a partial configuration layer will infer the entire object based on spatial interrelationship and final detection results are obtained from the fusion of these possible entire objects using a weighted continuous clustering method. As PCM makes a better compromise between the deformation modeling flexibility of small parts and the discriminative shape-capturing capability of large DPM, its performance on occluded object detection will be improved. Moreover, occlusion states of detected objects can be inferred with the intermediate results of our model. Experimental results on multiple high-resolution remote sensing image datasets demonstrate the effectiveness of the proposed model.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Geometric Calibration of an Aerial Multihead Camera System for Direct
           Georeferencing Applications
    • Authors: Leonardo E. Filho;Edson A. Mitishita;Ana Paula B. Kersting;
      Pages: 1926 - 1937
      Abstract: An aerial multihead camera consists of a photogrammetric system composed of multiple cameras, which are mounted together in a main structure. By combining the images acquired simultaneously from each camera, a single synthetic image with much larger coverage can be generated. Such systems are usually integrated with a position and orientation system (POS) to perform direct georeferencing (DG) or integrated sensor orientation (ISO). However, to obtain mapping products with high accuracy through a DG procedure, it is essential the implementation of the system geometric calibration. Usually, the aerial multihead camera manufacturers perform the geometric calibration using laboratory methods and only the camera interior orientation parameters (IOPs) and their relative orientation parameters (ROPs) are determined to generate the synthetic image (process known as “platform calibration”). The mounting parameters (lever arms and boresight misalignment angles) relating the synthetic image and GNSS/INS reference systems are usually defined using nominal installation values. The objective of this paper is to present an in-flight calibration methodology for multihead camera systems and its assessment for DG applications. The introduced methodology involves three steps: determination of the cameras' IOPs and their ROPs; synthetic image generation; and refinement of the IOPs of the synthetic image and the mounting parameters determination between the synthetic image and GNSS/INS reference systems using different methods. The results of the experiments shown the viability of the proposed methodology for DG applications involving photogrammetric procedures for large-scale mapping requirements.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Subpixel Mapping of Urban Areas Using EnMAP Data and Multioutput Support
           Vector Regression
    • Authors: Johannes Rosentreter;Ron Hagensieker;Akpona Okujeni;Ribana Roscher;Paul D. Wagner;Björn Waske;
      Pages: 1938 - 1948
      Abstract: Hyperspectral remote sensing data offer the opportunity to map urban characteristics in detail. Though, adequate algorithms need to cope with increasing data dimensionality, high redundancy between individual bands, and often spectrally complex urban landscapes. The study focuses on subpixel quantification of urban land cover compositions using simulated environmental mapping and analysis program (EnMAP) data acquired over the city of Berlin, utilizing both machine learning regression and classification algorithms, i.e., multioutput support vector regression (MSVR), standard support vector regression (SVR), import vector machine classifier (IVM), and support vector classifier (SVC). The experimental setup incorporates a spectral library and a reference land cover fraction map used for validation purposes. The library spectra were synthetically mixed to derive quantitative training data for the classes vegetation, impervious surface, soil, and water. MSVR and SVR models were trained directly using the synthetic mixtures. For IVM and SVC, a modified hyperparameter selection approach is conducted to improve the description of urban land cover fractions by means of probability outputs. Validation results demonstrate the high potential of the MSVR for subpixel mapping in the urban context. MSVR outperforms SVR in terms of both accuracy and computational time. IVM and SVC work similarly well, yet with lower accuracies of subpixel fraction estimates compared to both regression approaches.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Centralized Collaborative Sparse Unmixing for Hyperspectral Images
    • Authors: Rui Wang;Heng-Chao Li;Wenzhi Liao;Xin Huang;Wilfried Philips;
      Pages: 1949 - 1962
      Abstract: Spectral unmixing is very important in hyperspectral image analysis and processing, which aims at identifying the constituent spectra (i.e., endmembers) and estimating their fractional abundances from the mixed pixels. In recent years, sparse unmixing has received considerable interest. However, the acquired hyperspectral images are generally degraded by the noise, making sparse unmixing not faithful enough. To address this issue, this paper proposes a novel framework to couple sparse hyperspectral unmixing and abundance estimation error reduction together. Specifically, with the definition of abundance estimation error, a centralized constraint is incorporated into the collaborative sparse unmixing framework by exploiting the nonlocal redundancy of abundance map. This way we suppress the abundance estimation error, and improve the unmixing accuracy. Meanwhile, the alternating direction method of multipliers is introduced to solve the underlying constrained model. Experimental results on both synthetic and real hyperspectral data demonstrate the effectiveness of our proposed algorithm.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Hyperspectral Image Superresolution by Transfer Learning
    • Authors: Yuan Yuan;Xiangtao Zheng;Xiaoqiang Lu;
      Pages: 1963 - 1974
      Abstract: Hyperspectral image superresolution is a highly attractive topic in computer vision and has attracted many researchers' attention. However, nearly all the existing methods assume that multiple observations of the same scene are required with the observed low-resolution hyperspectral image. This limits the application of superresolution. In this paper, we propose a new framework to enhance the resolution of hyperspectral images by exploiting the knowledge from natural images: The relationship between low/high-resolution images is the same as that between low/high-resolution hyperspectral images. In the proposed framework, the mapping between low- and high-resolution images can be learned by deep convolutional neural network and be transferred to hyperspectral image by borrowing the idea of transfer learning. In addition, to study the spectral characteristic between low- and high-resolution hyperspectral image, collaborative nonnegative matrix factorization (CNMF) is proposed to enforce collaborations between the low- and high-resolution hyperspectral images, which encourages the estimated solution to extract the same endmembers with low-resolution hyperspectral image. The experimental results on ground based and remote sensing data suggest that the proposed method achieves comparable performance without requiring any auxiliary images of the same scene.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification
    • Authors: Bin Pan;Zhenwei Shi;Xia Xu;
      Pages: 1975 - 1986
      Abstract: Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this paper, a novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant. In R-VCANet, the inherent properties of HSI data, spatial information and spectral characteristics, are utilized to construct the network. And by this means the obtained model could generate more powerful feature expression with less samples. First, spectral and spatial information are combined via the RGF, which could explore the contextual structure features and remove small details from HSI. More importantly, we have designed a new network called vertex component analysis network for deep features extraction from the smoothed HSI. Experiments on three popular datasets indicate that the proposed R-VCANet based method reveals better performance than some state-of-the-art methods, especially when the training samples available are not abundant.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Hyperspectral Image Classification Using Metric Learning in
           One-Dimensional Embedding Framework
    • Authors: Huiwu Luo;Yuan Yan Tang;Yulong Wang;Jianzhong Wang;Robert P. Biuk-Aghai;Jianjia Pan;Runzong Liu;Lina Yang;
      Pages: 1987 - 2001
      Abstract: Hyperspectral image (HSI) classification has become an active research area in the remote sensing field. In order to construct a simple and reliable classifier, learning an adequate distance metric from a given HSI dataset is still a critical and challenging task in many HSI applications. In this paper, a novel distance metric learning (DML) framework based on 1-D manifold embedding (1DME), named DL1DME, is proposed for HSI classification. The 1DME framework was developed by using the recently developed smooth ordering technique. This framework enables us to elaborately exploit the benefits of DML in the development of the 1DME algorithm. The core of the state-of-the-art DML is to learn a Mahalanobis matrix from the given dataset that better describes the similarity between pixels. Largest margin nearest neighbors (LMNN) and information theoretic metric learning (ITML) are employed for the Mahalanobis matrix learning. Then, based on the affinity defined by the Mahalanobis matrix, the preclassifiers are constructed using the simple 1-D regularization on 1DME; and they predict the labels of the test data. By a voting rule, the pixels labeled in the same class by most of the preclassifiers are voted into the confidently predicted set, which are then merged with the current labeled set. The labeled set enlargement process is repeated if the original one has a very small size. The final classifier is then constructed in the 1DME framework again, but based on the enlarged labeled set. According to the aforementioned strategy, two novel DML-based 1DME classification algorithms, DL1DME-LMNN and DL1DME-ITML, are developed in this paper. Experimental results on three popular real-world HSIs demonstrate that the classification performance of the proposed DL1DME is superior to other most popular SSL methods in terms of classification accuracies.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Robust Matrix Discriminative Analysis for Feature Extraction From
           Hyperspectral Images
    • Authors: Renlong Hang;Qingshan Liu;Yubao Sun;Xiaotong Yuan;Hucheng Pei;Javier Plaza;Antonio Plaza;
      Pages: 2002 - 2011
      Abstract: Linear discriminative analysis (LDA) is an effective feature extraction method for hyperspectral image (HSI) classification. Most of the existing LDA-related methods are based on spectral features, ignoring spatial information. Recently, a matrix discriminative analysis (MDA) model has been proposed to incorporate the spatial information into the LDA. However, due to sensor interferers, calibration errors, and other issues, HSIs can be noisy. These corrupted data easily degrade the performance of the MDA. In this paper, a robust MDA (RMDA) model is proposed to address this important issue. Specifically, based on the prior knowledge that the pixels in a small spatial neighborhood of the HSI lie in a low-rank subspace, a denoising model is first employed to recover the intrinsic components from the noisy HSI. Then, the MDA model is used to extract discriminative spatial-spectral features from the recovered components. Besides, different HSIs exhibit different spatial contextual structures, and even a single HSI may contain both large and small homogeneous regions simultaneously. To sufficiently describe these multiscale spatial structures, a multiscale RMDA model is further proposed. Experiments have been conducted using three widely used HSIs, and the obtained results show that the proposed method allows for a significant improvement in the classification performance when compared to other LDA-based methods.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Similarity-Based Multiple Kernel Learning Algorithms for Classification of
           Remotely Sensed Images
    • Authors: Saeid Niazmardi;Abdolreza Safari;Saeid Homayouni;
      Pages: 2012 - 2021
      Abstract: Multiple kernel learning (MKL) algorithms are proposed to address the problems associated with kernel selection of the kernel-based classification algorithms. Using a group of kernels rather than one single kernel, the MKL algorithms aim to provide better classification efficiency. This paper presents new similarity-based MKL algorithms to classify remote-sensing images. These algorithms find the optimal combination of kernels by maximizing the similarity between a combination of kernels and an ideal kernel. In this framework, we initially introduced three similarity measures to be used: kernel alignment, norm of kernel difference, and Hilbert-Schmidt independence criterion. Then, we proposed to solve the optimization problems of the MKL algorithm associated with each similarity measure adopting heuristic and convex optimization methods. The performances of the proposed algorithms were compared with a single kernel support vector machines as well as other MKL algorithms for classifying the features extracted from the high-resolution and hyperspectral images. The results demonstrated that the similarity-based MKL algorithms performed better than other algorithms, especially when their optimization problems were solved using the convex optimization methods or when few training samples were available. Moreover, when the optimization problems of these algorithms were solved using the heuristic optimization methods, they were able to yield acceptable performances and were faster than other MKL algorithms.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Iterative Reweighting Heterogeneous Transfer Learning Framework for
           Supervised Remote Sensing Image Classification
    • Authors: Xue Li;Liangpei Zhang;Bo Du;Lefei Zhang;Qian Shi;
      Pages: 2022 - 2035
      Abstract: Supervised classification methods have been widely used in the hyperspectral remote sensing image analysis. However, they require a large number of training samples to guarantee good performance, which costs a large amount of time and human labor, motivating researchers to reuse labeled samples from the mass of pre-existing related images. Transfer learning methods can adapt knowledge in the existing images to solve the classification problem in new yet related images, and have drawn increasing interest in the remote sensing field. However, the existing methods in the RS field require that all the images share the same dimensionality, which prevents their practical application. This paper focuses on the transfer learning problem for heterogeneous spaces where the dimensions are different. We propose a novel iterative reweighting heterogeneous transfer learning (IRHTL) framework that iteratively learns a common space for the source and target data and conducts a novel iterative reweighting strategy to reweight the source samples. In each iteration, the heterogeneous data are first mapped into a common space by two projection functions based on a weighted support vector machine. Second, based on the common subspace, the source data are reweighted by using the iterative reweighting strategy and reused for the transferring, according to their relative importance. Experiments undertaken on three data sets confirmed the effectiveness and reliability of the proposed IRHTL method.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Sparse Representation-Based Hyperspectral Data Processing: Lossy
    • Authors: Hairong Wang;Turgay Celik;
      Pages: 2036 - 2045
      Abstract: This paper presents a method for lossy hyperspecral data compression based on sparse representation. The idea is to learn a dictionary that induces sparsity in the coefficient vectors that represent new input signals. The energy compaction feature of such sparse coefficient vectors is then evaluated in a lossy hyperspectral data compression framework. Experimental results on a number of hyperspectral data show that this approach is effective in hyperspectral data compression, and comparable to some of the state-of-the-arts data compression schemes, such as JPEG2000 with multiple component transformations and three-dimensional-set partitioning in hierarchical trees. Specifically, using the proposed framework, dictionaries that exploit spectral correlation, or spectral and spatial correlations, are trained using online dictionary learning. A hyperspectral data is represented using the learned dictionary via sparse coding. The resulting sparse coefficients are then encoded to formulate the final bit stream. The proposed framework allows using a base dictionary trained offline, or incorporating an update to the base dictionary, to achieve more adaptivity.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Sparse Spatio-Spectral LapSVM With Semisupervised Kernel Propagation for
           Hyperspectral Image Classification
    • Authors: Lixia Yang;Min Wang;Shuyuan Yang;Rui Zhang;Pingting Zhang;
      Pages: 2046 - 2054
      Abstract: The information contained in the hyperspectral data allows the characterization, identification, and classification of the land covers with improved accuracy and robustness. Many methods have been explored in the hyperspectral image classification (HIC). Among these methods, spatio-spectral Laplacian support vector machine (SS-LapSVM) combines the spatial and spectral information on both the labeled and unlabeled samples together through the weight sum of a spectral regularization term and a spatial regularization term. Thus, it can achieve accurate classification with very few labeled samples and has proved to be effective in HIC. In this paper, a sparse SS-LapSVM with semisupervised Kernel Propagation (S3LapSVM-KP) is constructed to achieve higher accuracy and efficiency in HIC. First, data-driven semisupervised KP is proposed to carefully learn a kernel matrix from a small number of labeled pixels. Furthermore, a one-step sparse pruning algorithm is advanced by solving sparse weight vectors associated with network nodes in SS-LapSVM. By combining semisupervised KP with sparse coding, S3LapSVM-KP can not only automatically determine kernels from data, but also avoid overfitting and reduce computation cost resulted from the nonsparse topology of SS-LapSVM. The performance of S3LapSVM-KP is evaluated on several real hyperspectral datasets, and the results show that S3LapSVM-KP can achieve accurate and rapid classification with very few labeled data.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR
           Data Classification
    • Authors: Maher Ibrahim Sameen;Biswajeet Pradhan;Helmi Z. M. Shafri;Mustafa Ridha Mezaal;Hussain bin Hamid;
      Pages: 2055 - 2066
      Abstract: Light detection and ranging (LiDAR) data classification provides useful thematic maps for numerous geospatial applications. Several methods and algorithms have been proposed recently for LiDAR data classification. Most studies focused on object-based analysis because of its advantages over per-pixel-based methods. However, several issues, such as parameter optimization, attribute selection, and development of transferable rulesets, remain challenging in this topic. This study contributes to LiDAR data classification by developing an approach that integrates ant colony optimization (ACO) and rule-based classification. First, LiDAR-derived digital elevation and digital surface models were integrated with high-resolution orthophotos. Second, the processed raster was segmented with the multiresolution segmentation method. Subsequently, the parameters were optimized with a supervised technique based on fuzzy analysis. A total of 20 attributes were selected based on general knowledge on the study area and LiDAR data; the best subset containing 12 attributes was then selected via ACO. These attributes were utilized to develop rulesets through the use of a decision tree algorithm, and a thematic map was generated for the study area. Results revealed the robustness of the proposed method, which has an overall accuracy of ~95% and a kappa coefficient of 0.94. The rule-based approach with all attributes and the k nearest neighbor (KNN) classification method were applied to validate the results of the proposed method. The overall accuracy of the rule-based method with all attributes was ~88% (kappa = 0.82), whereas the KNN method had an overall accuracy of
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Toward Robust Segmentation Results Based on Fusion Methods for Very High
           Resolution Optical Image and LiDAR Data
    • Authors: Mohamad M. Awad;
      Pages: 2067 - 2076
      Abstract: Using very high resolution remote sensing images to extracting urban features from very high resolution remote sensing images is a very complex and difficult task. The improvement in geospatial technologies brought forward many solutions that can help in improving the process of urban feature extraction. Data collection using light detection and ranging (LiDAR) and capturing very high resolution optical images concurrently is one of these solutions. This research proves that the fusion of high-resolution optical image with LiDAR data can improve image processing results. It is based on increasing urban features extraction success rate by reducing oversegmentation. The fusion process relies first on wavelet transform techniques, which are run several times with different parameters (rules). Then, an innovative technique is implemented to improve fusion process. The two techniques are compared, and both have reduced fragmented segments and created homogeneous urban features. However, the fused image with the innovative technique has improved the accuracy of the segmentation results. The average accuracy for building detection is 96% (maximum 100% and minimum 92%) using the innovative technique compared to 21% and 51% for no fusion and wavelet-fusion-based techniques. Furthermore, an index is used to measure the quality of the building details which are detected after using the innovative fusion technique. The result indicates that the quality index is greater or equal to 86%.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Proceedings of the IEEE
    • Pages: 2079 - 2079
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
  • Become a published author in 4 to 6 weeks
    • Pages: 2080 - 2080
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: May 2017
      Issue No: Vol. 10, No. 5 (2017)
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