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  Subjects -> ELECTRONICS (Total: 158 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 2)
Advances in Electronics     Open Access   (Followers: 6)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Microelectronic Engineering     Open Access   (Followers: 7)
Advances in Power Electronics     Open Access   (Followers: 11)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 110)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 13)
Annals of Telecommunications     Hybrid Journal   (Followers: 5)
APL : Organic Electronics and Photonics     Hybrid Journal   (Followers: 2)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 8)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 16)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 14)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 23)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 5)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 6)
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: 14)
China Communications     Full-text available via subscription   (Followers: 5)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 42)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 30)
Electronic Markets     Hybrid Journal   (Followers: 5)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 12)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics Letters     Hybrid Journal   (Followers: 18)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 28)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 3)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 9)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal  
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 19)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 2)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 28)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 22)
IEEE Consumer Electronics Magazine     Full-text available via subscription   (Followers: 22)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 18)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 4)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 21)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 21)
IEEE Transactions on Audio, Speech, and Language Processing     Hybrid Journal   (Followers: 12)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 38)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 19)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 7)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 14)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 36)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 3)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 7)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 5)
IET Power Electronics     Hybrid Journal   (Followers: 17)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 11)
IETE Journal of Education     Open Access   (Followers: 1)
IETE Journal of Research     Open Access   (Followers: 7)
IETE Technical Review     Open Access   (Followers: 4)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 15)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 3)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 5)
International Journal of Advanced Electronics and Communication Systems     Open Access   (Followers: 5)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 3)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 12)
International Journal of Antennas and Propagation     Open Access   (Followers: 5)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 1)
International Journal of Biomedical Nanoscience and Nanotechnology     Hybrid Journal   (Followers: 5)
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: 1)
International Journal of Electronics & Data Communication     Open Access   (Followers: 3)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 4)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 4)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 1)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 5)
International Journal of Nanoscience     Hybrid Journal   (Followers: 1)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 11)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 3)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 4)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 2)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 2)
International Journal on Communication     Full-text available via subscription   (Followers: 11)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 5)
International Transaction of Electrical and Computer Engineers System     Open Access  
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access  
Journal of Artificial Intelligence     Open Access   (Followers: 6)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 1)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 8)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 1)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 3)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 4)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 3)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 1)
Journal of Electronics (China)     Hybrid Journal   (Followers: 3)
Journal of Field Robotics     Hybrid Journal   (Followers: 1)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 94)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 2)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 5)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 5)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access  
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 6)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 6)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 10)
Journal of Signal and Information Processing     Open Access   (Followers: 6)
Jurnal Infotel     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 3)
Microelectronics and Solid State Electronics     Open Access   (Followers: 11)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 26)
Nanotechnology, Science and Applications     Open Access   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access  
Optical Communications and Networking, IEEE/OSA Journal of     Hybrid Journal   (Followers: 6)
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: 3)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 2)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 1)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 1)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 15)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 33)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 7)
Solid-State Electronics     Hybrid Journal   (Followers: 5)
Superconductor Science and Technology     Hybrid Journal  
Synthesis Lectures on Power Electronics     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 3)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 3)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access  
Wireless Power Transfer     Full-text available via subscription  
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 8)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.632]   [H-I: 19]   [15 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by Institute of Electrical and Electronics Engineers (IEEE) Homepage  [177 journals]
  • Front Cover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Institutional Listings
    • Abstract: The GRSS society invites application for Institutional Listings from firms interested.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Table of Contents
    • Pages: 1321 - 1322
      Abstract: Presents the table of contents for this issue of the publication.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • An Improved Method for Deriving Daily Evapotranspiration Estimates From
           Satellite Estimates on Cloud-Free Days
    • Authors: Wu; B.;Zhu, W.;Yan, N.;Feng, X.;Xing, Q.;Zhuang, Q.;
      Pages: 1323 - 1330
      Abstract: An improved method, based on the daily surface resistance, is proposed to extend satellite evapotranspiration (ET) on a clear day into ET for each and every day. Alternative climatic variables such as soil moisture, wind speed, and net radiation are explored for estimating daily surface resistance using a Penman-Monteith (P-M) formulation. The study was carried out for the Yingke (YK) oasis plains area (maize cropland) and the Arou (AR) alpine meadow area (grassland) located in the midstream and upstream, respectively, of the Heihe River Basin of northwestern China. Statistical results show that the proposed method performs well for estimating daily ET for both study areas, with results slightly superior in the midstream, cropland area where the coefficient of determination (R2) was 0.9249 and the index of agreement (d) was 0.978. In the upstream alpine meadow area, the coefficient of determination (R2) was 0.9074, and the index of agreement (d) was 0.961. The proposed method provides an enhanced approach for estimating daily ET in the ETWatch model. Future work will focus on scaling this improved method to the estimation of regional daily ET map.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Analysis of Orientation Angle Shifts on the Polarimetric Data Using
           Radarsat2 Images
    • Authors: Souissi; B.;Ouarzeddine, M.;
      Pages: 1331 - 1342
      Abstract: Polarization orientation angle (POA) shifts induced by the variations of range and azimuth slopes cause the polarization to rotate about the radar line of sight. Their existence reduces the accuracy measurement of geophysical parameters from polarimetric synthetic aperture radar (PolSAR) images and may generate erroneous scattering characteristics that could be misinterpreted. In real situations, terrain slopes rotate the polarization basis of the polarimetric scattering matrices by an orientation angle shift, and induce significant cross-polarization power. Consequently, it is desirable to compensate the data for the orientation effect before applying scattering model-based decompositions. In this paper, we investigate the compensation effect on the elements of the coherency matrix using the orientation angle extracted from circular polarization technique and from the copolarization signature applied to building areas. The effect of this compensation is that the volume scattering power is consistently decreased, while the double-bounce power is increased. The surface and helix scattering powers are roll invariant. Comparing both methods, we find that the circular polarization algorithm gives mostly the best results except for some targets. In this way, a combined use of both algorithms has been proposed to choose an optimum orientation angle, which can be used directly to compensate POLSAR data to ensure improvement in the overall polarimetric decomposition and classification. We illustrate our results using the polarimetric SAR images acquired on the Algiers city by the RadarSAT2 (FQ19) in C-band.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Performances of a Microwave Tomographic Algorithm for GPR Systems Working
           in Differential Configuration
    • Authors: Persico; R.;Pochanin, G.;Ruban, V.;Orlenko, A.;Catapano, I.;Soldovieri, F.;
      Pages: 1343 - 1356
      Abstract: The imaging capabilities achieved from a ground-penetrating radar (GPR) system based on a contactless differential measurement configuration and from a microwave tomographic approach are investigated. The system is made up of two receiving antennas located at different heights and placed symmetrically with respect to the transmitting antenna. The data, obtained as the difference between the signals measured by the two receivers, are processed by means of a microwave tomographic approach, which is based on a linear model of the electromagnetic scattering and is specifically designed for the differential GPR system. The achievable reconstruction capabilities are theoretically investigated and their dependence on the offset between the receiving antennas is taken into account in order to provide hints about the preferable offset. The theoretical analysis corroborates that, in the case at hand, the reconstructed images are affected by a spatial filtering effect and the retrievable spectral content is reduced by “hyperbolas of zeroes,” whose occurrence increases with the spatial offset between the two receiving antennas. The effectiveness of the proposed GPR system and of the theoretical analysis is assessed against both synthetic and experimental data.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Assimilation of Active and Passive Microwave Observations for Improved
           Estimates of Soil Moisture and Crop Growth
    • Authors: Liu; P.-W.;Bongiovanni, T.;Monsivais-Huertero, A.;Judge, J.;Steele-Dunne, S.;Bindlish, R.;Jackson, T.J.;
      Pages: 1357 - 1369
      Abstract: An ensemble Kalman Filter-based data assimilation framework that links a crop growth model with active and passive (AP) microwave models was developed to improve estimates of soil moisture (SM) and vegetation biomass over a growing season of soybean. Complementarities in AP observations were incorporated to the framework, where the active observations were used to optimize surface roughness and update vegetation biomass, while passive observations were used to update SM. The framework was implemented in a rain-fed agricultural region of the southern La-Plata Basin during the 2011-2012 growing season, through a synthetic experiment and AP observations from the Aquarius mission. The synthetic experiment was conducted at a temporal resolution of 3 and 7 days to match the current AP missions. The assimilated estimates of SM in the root zone and dry biomass were improved compared to those from the cases without assimilation, during both 3- and 7-day assimilation scenarios. Particularly, the 3-day assimilation provided the best estimates of SM in the near surface and dry biomass with reductions in RMSEs of 41% and 42%, respectively. The absolute differences of assimilated LAI from Aquarius were $
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Freeze/Thaw Detection and Validation Using Aquarius’ L-Band
           Backscattering Data
    • Authors: Xu; X.;Derksen, C.;Yueh, S.H.;Dunbar, R.S.;Colliander, A.;
      Pages: 1370 - 1381
      Abstract: The seasonal cycle of landscape freeze/thaw (FT) state across mid- to high latitudes influences critical processes such as the land surface energy balance, carbon cycle dynamics related to vegetation growth, and hydrological partitioning between surface runoff and infiltration. In this paper, we produce the first daily FT classification for the 2011-2014 period based on L-band radar measurements from Aquarius. The radar FT algorithm used in this paper is based on a seasonal threshold approach, which is also the baseline algorithm applied to higher-resolution (3 km) radar measurements from NASA's Soil Moisture Active/Passive (SMAP) mission (Launched January 31, 2015). The lower frequency (L-band) radar backscatter measurements from Aquarius provide enhanced sensitivity to FT conditions in vegetation canopy, snow and surface soil layers, although the relative radar penetration depth and sensitivity of the FT signal to these landscape elements will vary according to surface moisture and vegetation biomass conditions, and underlying land cover and terrain heterogeneity [1], [2]. Evaluation of the seasonal threshold FT algorithm using Aquarius was performed using surface air and soil temperatures from selected stations in the Snow Telemetry (SnoTel) network. Analysis identified good agreement during the fall freeze-up period with flag agreement exceeding the 80% SMAP accuracy target when summarized on a monthly basis. Disagreement was greater during the spring thaw transition due in part to uncertainty in characterizing thaw from in situ measurements. Unlike the fall season, stronger agreement in the spring was identified when the reference state was characterized with air temperature compared to soil temperature.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Observations of Cutting Practices in Agricultural Grasslands Using
           Polarimetric SAR
    • Authors: Voormansik; K.;Jagdhuber, T.;Zalite, K.;Noorma, M.;Hajnsek, I.;
      Pages: 1382 - 1396
      Abstract: In this study, TerraSAR-X dual-polarimetric HH/VV and RADARSAT-2 fully polarimetric synthetic aperture radar (SAR) parameters are compared to results from field surveys of grasslands in order to establish a methodology for the detection of grass cutting events. The experiment over grasslands in Estonia during the vegetative season of 2013 was carried out with an extensive survey measuring grass height, wet and dry biomass, and soil moisture. An entropy/alpha decomposition was applied to the data. Additionally, the polarimetric coherences between the different channels, backscatter levels, and intensity ratios were analyzed. From the numerous polarimetric parameters studied, HH/VV polarimetric coherence and the scattering entropy seemed to provide the most reliable indication about a cutting event based on the polarimetric SAR (PolSAR) time series. The behavior was more pronounced in TerraSAR-X data than in RADARSAT-2 data, possibly due to the finer temporal sampling (11 days vs. 24 days for exactly the same imaging geometry) and shorter wavelength. After the grass was cut, TerraSAR-X HH/VV polarimetric coherence magnitude decreased by 0.27 (by 0.12 on average) and scattering entropy increased by 0.21 (by 0.07 on average). The observed behavior can be well explained by field observations and modeling of the vegetation backscattering. According to a vegetation particle model, growing grass (mainly vertically oriented dipoles) corresponds to lower entropy and higher HH/VV polarimetric coherence magnitude than cut grass (more horizontally and more randomly oriented dipoles). This indicates the potential to use HH/VV polarimetric coherence magnitude and scattering entropy for the monitoring of grassland cutting practices.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Adaptive Secondary Range Compression Algorithm in Geosynchronous SAR
    • Authors: Hu; C.;Tian, Y.;Zeng, T.;Long, T.;Dong, X.;
      Pages: 1397 - 1413
      Abstract: In geosynchronous synthetic aperture radar (GEO SAR), due to larger equivalent squint angle and extremely complicated geometrical relationship between satellite motion and earth rotation at equator, the Doppler parameter space-variance becomes further strained, i.e., its value gets larger and its direction becomes uncertain. Meanwhile, the large imaging area brings in additional difficulties to compensate the Doppler parameter. In addition, since the synthetic aperture time is up to hundred seconds, the assumptions of the linear trajectory model and the Fresnel approximation appear to be decreasingly effective in GEO SAR, and the range cell migration and two-dimensional coupling also become larger. In allusion to the problems mentioned above, this paper proposes an improved secondary range compression (SRC) algorithm. First, special issues of GEO SAR imaging are analyzed, such as the Doppler parameter space-variance, the error of the linear trajectory model and the Fresnel approximation. Then, the effects of Doppler parameter space-variance on GEO SAR imaging are analyzed. Finally, the core issue of GEO SAR imaging at equator, i.e., adaptive phase compensation, is discussed in detail. The direction of Doppler parameter space-variance is determined, and the effects of Doppler parameter space-variance are compensated by sub-block processing, the processing is operated along the direction of Doppler parameter space-variance at an interval of calculated scale. Simulations of point array targets and area targets at equator are performed, and the correctness of this algorithm is validated.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Multiple Scattering Effects With Cyclical Correction in Active Remote
           Sensing of Vegetated Surface Using Vector Radiative Transfer Theory
    • Authors: Liao; T.-H.;Kim, T.-H.;Tan, S.;Tsang, L.;Su, C.;Jackson, T.J.;
      Pages: 1414 - 1429
      Abstract: The energy transport in a vegetated (corn) surface layer is examined by solving the vector radiative transfer equation using a numerical iterative approach. This approach allows a higher order that includes the multiple scattering effects. Multiple scattering effects are important when the optical thickness and scattering albedo of the vegetation layer are large. When both the albedo and the optical thickness exceed 0.4, higher orders contribute significantly (e.g., vertical polarization at L-band). The approach is applied to vegetated surfaces using typical crop structure for backscattering from L-band to Ku-band. For corn fields at L-band, multiple scattering effects are more important for vertical scattered wave with vertical incidence (VV). For example, when vegetation water content (VWC) is 3kg/m2, the deviation between first order and multiple scattering for corn field for VV could be 3.5 dB while 0.7 dB for horizontal scattered wave with horizontal incidence (HH). The iterative approach also allows the separation of the contribution to backscattering from each scattering order and scattering mechanism. Each scattering mechanism is associated with a unique scattering path. By examining the duality of the paths, we are able to identify the cyclical terms with existence of a reflective boundary. The cyclical correction to the backscattering accounts for backscattering enhancement effects on the copolarization by a factor of two. The approach is validated against the SMAPVEX12 L-band corn dataset over the entire crop growth and large soil moisture variations. The model prediction matches the observation with 1.93 and 1.46 dB root-mean-square error (RMSE) for VV and HH, respectively, while correlations are 0.67 and 0.88, respectively. Time-series retrieval is also applied successfully for both soil moisture and VWC with 0.06 cm3/cm3 and 0.44 kg/m2 RMSE, respectively, while correlations are 0.7 and 0.92, respectively. Fo- large VWC, this approach corrects the underestimated backscatters in the single scattering caused by large attenuation.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Evaluation of Disaggregation Methods for Downscaling MODIS Land Surface
           Temperature to Landsat Spatial Resolution in Barrax Test Site
    • Authors: Bisquert; M.;Sanchez, J.;Caselles, V.;
      Pages: 1430 - 1438
      Abstract: Thermal infrared (TIR) data are usually acquired at a coarser spatial resolution (CR) than visible and near infrared (VNIR). Several disaggregation methods have been recently developed to enhance the TIR spatial resolution using VNIR data. These approaches are based on the retrieval of a relation between TIR and VNIR data at CR, or training of a neural network, to be applied at the fine resolution afterward. In this work, different disaggregation methods are applied to the combination of two different sensors in the experimental test site of Barrax, Spain. The main objective is to test the feasibility of these techniques when applied to satellites provided with no TIR bands. Landsat and moderate imaging spectroradiometer (MODIS) images were used for this work. Land surface temperature (LST) from MODIS images was disaggregated to the Landsat spatial resolution using Landsat VNIR data. Landsat LST was used for the validation and comparison of the different techniques. Best results were obtained by the method based on a linear regression between normalized difference vegetation index (NDVI) and LST. An average RMSE = ±1.9 K was observed between disaggregated and Landsat LST from four different dates in a study area of 120 km2.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Large Polarimetric SAR Data Semi-Supervised Classification With
           Spatial-Anchor Graph
    • Authors: Liu; H.;Wang, Y.;Yang, S.;Wang, S.;Feng, J.;Jiao, L.;
      Pages: 1439 - 1458
      Abstract: Recently, graph-based semi-supervised classification (SSC) has attracted considerable attentions as it could enhance classification accuracy by utilizing only a few labeled samples and large numbers of unlabeled samples via graphs. However, the construction of graphs is time consuming especially for large-scale polarimetric synthetic aperture radar (PolSAR) data. Moreover, speckle noise in images remarkably degrades the accuracy of the constructed graph. To address these two issues, this paper proposes a novel spatial-anchor graph for large-scale PolSAR terrain classification. First, the PolSAR image is segmented to obtain homogeneous regions. The features of each pixel are weighted by that of the surrounding pixels from the homogeneous regions to reduce the influence of the speckle noise. Second, Wishart distance-based clustering is performed on the weighted features, and the cluster centers are computed and serve as initial anchors. Then, the label of each pixel is predicted by the label of its nearest anchors on the spatial-anchor graph which is constructed through solving an optimization problem. Experimental results on synthesized PolSAR data and real ones from different approaches show that the proposed method reduces the computational complexity to a linear time, and the graph combined with the spatial information suppresses the speckle noise and enhances the classification accuracy in comparison with state-of-the-art graph-based SSCs when only a small number of labeled samples are available.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Man-Made Target Detection from Polarimetric SAR Data via Nonstationarity
           and Asymmetry
    • Authors: Xiang; D.;Tang, T.;Ban, Y.;Su, Y.;
      Pages: 1459 - 1469
      Abstract: Detection of man-made targets in urban areas using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area since it has a close relationship with urban planning, rescue service, etc. This paper presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Nonstationarity in azimuth direction is already utilized to separate man-made and natural targets in urban areas. However, there are still some drawbacks. Some small man-made targets and roads cannot be effectively detected. In addition, nonstationarity can also occur in some other natural surfaces, such as cropland with Bragg resonance. Therefore, to resolve these problems, we incorporate reflection asymmetry into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets. Airborne ESAR data and spaceborne PALSAR data are used to validate the performance of the proposed method. The result obtained by our proposed method shows a 20% higher accuracy than the result based on original nonstationarity extraction method. Natural areas with Bragg resonance are removed. Moreover, most of the buildings and some metallic fences along the road can also be accurately detected.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • A Local Search-Based GeneSIS algorithm for the Segmentation and
           Classification of Remote-Sensing Images
    • Authors: Mylonas; S.K.;Stavrakoudis, D.G.;Theocharis, J.B.;Zalidis, G.C.;Gitas, I.Z.;
      Pages: 1470 - 1492
      Abstract: A local search-based version of the so-called genetic sequential image segmentation (GeneSIS) algorithm is presented in this paper, for the classification of remotely sensed images. The new method combines the properties of the GeneSIS framework with the principles of the region growing segmentation algorithms. Localized GeneSIS operates on a fine-segmented image obtained after preliminary watershed transformation. Segmentation proceeds by iterative expansions emanating from object cores, i.e., connected components of marked watersheds. At each expansion trial, the process involves three successively performed operations: 1) generation of the object's neighborhood to a specified order; 2) local exploration of the neighborhood through an evolutionary algorithm to identify the best expansion to be merged; and 3) rearrangement of the object neighborhoods. We propose two priority strategies for the selection of the objects to be expanded and two different modes of operation performing either supervised or semisupervised segmentation of the image. The combination of the priority strategies and segmentation modes lead to four different implementations of localized GeneSIS. Due to the local search approach adopted here, the resulting algorithms have considerably lower execution times, while at the same time, they provide comparable classification accuracies compared to those produced by previous GeneSIS variants. Experimental analysis is conducted using a hyperspectral forest image, a multispectral agricultural image, and the Pavia Centre image over an urban area. Comparative results are also provided with existing segmentation algorithms.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Application of a Combined Optical–Passive Microwave Method to
           Retrieve Soil Moisture at Regional Scale Over Chile
    • Authors: Santamaria-Artigas; A.;Mattar, C.;Wigneron, J.P.;
      Pages: 1493 - 1504
      Abstract: This work presents the calibration and evaluation of an optical-passive microwave method for retrieving soil moisture (SM) at regional scale using remote sensing and reanalysis data. Several data sets were used, such as the bipolarized brightness temperature provided by SM and Ocean Salinity (SMOS) L3 brightness temperature product, the Normalized Difference Vegetation Index (NDVI) from moderate resolution imaging spectroradiometer (MODIS), the soil temperature and water content of the first 0-7 cm of depth from the ERA-Interim reanalysis, and 13 land cover classes obtained from the ECOCLIMAP database. The method was applied over Chile between 28°S and 43°S for 2010-2012. The data set was used to calibrate and evaluate a semiempirical approach for estimating SM, first by using only the data from SMOS and ERA-Interim and then also including the MODIS vegetation indicator. Results were analyzed for every land cover class using the determination coefficient (r2), the coefficients obtained from the regressions, and the unbiased rootmean-square difference (ubRMSD). Results showed an increase in the average r2 for all classes when a vegetation index was used in the calibration of the approach. The increases in r2 ranged from 3% for the crop class, to 49% for the closed shrubland class. The ubRMSD presented a decrease in its value of up to 1% m3/m3 for the woodlands, open shrublands, and woody shrublands classes and up to 2% m3/m3 for the closed shrubland class. These results contribute to the use of single linear and semiempirical regressions to estimate SM at regional scale based on SMOS L-band bipolarized brightness temperature.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Three-Dimensional Interferometric ISAR Imaging for the Ship Target Under
           the Bi-Static Configuration
    • Authors: Wang; Y.;Li, X.;
      Pages: 1505 - 1520
      Abstract: In this paper, a novel three-dimensional (3-D) Interferometric inverse synthetic aperture radar (InISAR) imaging method, which especially works on the ship target, is presented. Considering the characteristics of the ship target and the 3-D InISAR system, the bistatic configuration is designed to avoid the imaging failure in special situations caused by the low velocity of the ship target. The specificity of the 3-D InISAR imaging technique makes the adoption of the optimal imaging time selection method based on the estimation of the Doppler center, which can help to select the imaging time when the cross-range resolution reaches maximum, effective and simple enough for the 3-D reconstruction of the ship target. Besides, the translational and angular motion compensations in such situations are explained clearly. Due to the shortness of the selected echoes caused by motion complexity of the ship target, the sparse Bayes learning method, which has a lot of advantages compared with other similar methods, is adopted to realize the super-resolution imaging for the purpose of separating the superposed scatterers and its superiority in reducing the computation load of the 3-D InISAR imaging system is demonstrated explicitly. Finally, the distortion of the reconstructed coordinate for the ship target caused by the bistatic configuration is corrected by the simplified coordinate transformation and the relationship between the coordinates of the ship target and the reconstructed 3-D model is explained in detail. Some experimental results are provided to substantiate the effectiveness of the 3-D InISAR imaging method for the ship target in this paper.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Unsupervised Quaternion Feature Learning for Remote Sensing Image
           Classification
    • Authors: Risojevic; V.;Babic, Z.;
      Pages: 1521 - 1531
      Abstract: Bag-of-words image representations based on local descriptors are common in image classification and retrieval tasks. However, in order to achieve state-of-the-art results, complex hand-crafted feature filters and/or support vector classifiers with nonlinear kernels are needed. Compared with hand-crafted features, unsupervised feature learning is a popular alternative, which results in feature filters adapted to the problem domain at hand. Although both color and intensity are important cues for remote sensing image classification and color images are commonly used for unsupervised feature learning, most of the existing algorithms do not take into account interrelationships between intensity and color information. We address this problem using quaternion representation for color images and propose unsupervised learning of quaternion feature filters, as well as feature encoding using quaternion orthogonal matching pursuit (Q-OMP). By using quaternion representation, we are able to jointly encode intensity and color information in an image. We obtain local descriptors by soft thresholding and computing absolute values of scalar and three vector parts of the quaternion-valued sparse code. Local descriptors are pooled, power-law transformed, and normalized, yielding the resulting image representation. The experimental results on UC Merced Land Use and Brazilian Coffee Scenes datasets are comparable or better than the state of the art, demonstrating the effectiveness of the proposed approach. The proposed method for quaternion feature learning is able to adapt to the characteristics of the available data, and being fully unsupervised, it emerges as a viable alternative to both hand-crafted representations and convolutional neural networks, especially in application scenarios with scarce-labeled training data.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Automatic Delineation of Clouds and Their Shadows in Landsat and CBERS
           (HRCC) Data
    • Pages: 1532 - 1542
      Abstract: The presence of clouds and their shadows is an obvious problem for maps obtained from multispectral images. As a matter of fact, clouds and their shadows create occluded and obscured areas, hence information gaps that need to be filled. The usual approach-pixel substitution-requires first to recognize the cloud/shadow pixels. This work presents a cloud/shadow delineation algorithm, the cloud/shadow delineation tool (CSDT) designed for Landsat and CBERS medium resolution multispectral data. The algorithm uses a set of literature indices, as well as a set of mathematical operations on the spectral bands, in order to enhance the visibility of the cloud/shadow objects. The performance of CSDT was tested on a set of scenes from the Landsat and CBERS catalogues. The obtained results showed more accurate and stable performance on Landsat data. In order to validate the proposed approach, this work presents also a comparison with the F-mask algorithm on Landsat scenes. Results show that the F-mask technique tends to overestimate the cloud cover, while CSDT slightly underestimates it. However, accuracy measures show a significantly better performance of the proposed method than the F-mask algorithm in our investigation.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • A New Imaging Algorithm for Forward-Looking Missile-Borne Bistatic SAR
    • Authors: Chen; S.;Yuan, Y.;Zhang, S.;Zhao, H.;Chen, Y.;
      Pages: 1543 - 1552
      Abstract: Bistatic synthetic aperture radar (BiSAR), which can break through the limitations of the conventional monostatic SAR on forward-looking imaging, continues to gain in significance due to a variety of applications, like the missile terminal guidance. To improve Doppler resolution and to avoid azimuth ambiguities of the missile-borne SAR, an extended nonlinear chirp scaling (NLCS) algorithm for the forward-looking missile-borne bistatic SAR (FLMB-SAR) is proposed in this paper. Based on the novel algorithm and the bistatic configuration with a stationary transmitter and a forward-looking missile-borne receiver, the missile-borne SAR not only can image the forward-looking terrain of the targets but also plays sufficient role in navigation. Finally, the simulation results are exhibited to validate the correctness of the analysis and prove the two-dimensional (2-D) imaging ability of the FLMB-SAR.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • The Generalized Additive Model for the Assessment of the Direct, Diffuse,
           and Global Solar Irradiances Using SEVIRI Images, With Application to the
           UAE
    • Authors: Ouarda; T.B.M.J.;Charron, C.;Marpu, P.;Chebana, F.;
      Pages: 1553 - 1566
      Abstract: Generalized additive models (GAMs) can model the nonlinear relationship between a response variable and a set of explanatory variables through smooth functions. GAM is used to assess the direct, diffuse, and global solar components in the United Arab Emirates (UAE), a country which has a large potential for solar energy production. Six thermal channels of the spinning enhanced visible and infrared imager (SEVIRI) instrument onboard Meteosat second generation (MSG) are used as explanatory variables along with the solar zenith angle, solar time, day number, and eccentricity correction. The proposed model is fitted using reference data from three ground measurement stations for the full year of 2010 and tested on two other stations for the full year of 2009. The performance of the GAM model is compared to the performance of the ensemble of artificial neural networks (ANNs) approach. Results indicate that GAM leads to improved estimates for the testing sample when compared to the bagging ensemble. GAM has the advantage over ANN-based models that we can explicitly define the relationships between the response variable and each explanatory variable through smooth functions. Attempts are made to provide physical explanations of the relations between irradiance variables and explanatory variables. Models in which the observations are separated as cloud-free and cloudy and treated separately are evaluated along with the combined dataset. Results indicate that no improvement is obtained compared to a single model fitted with all observations. The performance of the GAM is also compared to the McClear model, a physical-based model providing estimates of irradiance in clear sky conditions.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces
           Detection in Farmlands
    • Authors: Sofia; G.;Bailly, J.-S.;Chehata, N.;Tarolli, P.;Levavasseur, F.;
      Pages: 1567 - 1576
      Abstract: Among the most evident anthropogenic modifications of the landscape, terraces related to agricultural activities are ubiquitous structures that constitute important investments worldwide, and they recently acquired a new relevance to modern concerns about land-use management and erosion control. Conservation agriculture and terraces management are an application with great potentialities for Satellite Earth observation and the derived high-resolution topography. Due to its high agility, the Pleiades satellite constellation provides new, high-resolution digital elevation models (DEMs) with a submetric resolution that could be potentially useful for this task, and their application in a farmland context is nowadays an open research line. This work provides a first analysis, performing an automatic terrace mapping from DEMs obtained from Pleiades images, as compared to LiDAR DEMs. Two existing methods are considered: 1) the fast line segment detector (LSD) algorithm and 2) a geomorphometric method based on surface curvature. Despite the lower performances of Pleiades DEMs with respect to that of the LiDAR models, the results indicate that the Pleiades models can be used to automatically detect terrace slopes greater than 2 m with a detection rate of more than 80% of the total length of the terraces. In addition, the results showed that when using noisy DEMs, the geomorphometric method is more robust, and it slightly outperforms the LSD algorithm. These results provide a first analysis on how effective Pleiades DEMs can be as an alternative to LiDAR DEMs, also highlighting the future challenges for monitoring large extents in a farmland context.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • An RFI Index to Quantify the Contamination of SMOS Data by Radio-Frequency
           Interference
    • Authors: Soldo; Y.;Khazaal, A.;Cabot, F.;Kerr, Y.H.;
      Pages: 1577 - 1589
      Abstract: The quality of science data provided by ESA soil moisture and ocean salinity (SMOS) satellite is degraded by the presence of artificial sources emitting in the protected part of the L-band, which is preserved for passive measurements by ITU regulation. These sources appear as high temperature points in SMOS brightness temperature products (e.g., Level 1C products), and may affect the retrievals of both SMOS (e.g., Level 2 products). In this contribution, a method is presented to quantify the impact of radio-frequency interference (RFI) on each SMOS snapshots, through the definition of an “RFI index” based on the number, position, and intensity of the RFI sources present in the snapshot. The main purpose of RFI indices is to provide the user of SMOS scientific data with information to ease the RFI filtering, thus achieving more accurate results. The comparison of RFI indices with the outputs of two different methods providing similar snapshot-wise information about RFI shows that the use of RFI indices reduces the probability of missed RFI detections, without increasing the risk for false alarms.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Application of SMOS Soil Moisture and Brightness Temperature at High
           Resolution With a Bias Correction Operator
    • Authors: Kornelsen; K.C.;Davison, B.;Coulibaly, P.;
      Pages: 1590 - 1605
      Abstract: The assimilation of soil moisture and brightness temperature (TB) are expected to improve the modeling of land surface processes, but are only available at a resolution that is far coarser than the scale of many hydrological processes. Due to systematic differences between model states and satellite observations, a bias correction operator is a necessary step in land data assimilation schemes and was evaluated as a method to disaggregate coarse-scale satellite observations to fine-scale model grid cells (~800 m). This was done by coupling the Modélisation Environmentale Communautaire-Surface Hydrology (MESH) Hydrological Land-Surface Scheme to the Community Microwave Emissions Model (CMEM) to simulate soil moisture and TB. By comparison, MESH-CMEM was found to be in good agreement with observations from the Soil Moisture and Ocean Salinity (SMOS) satellite at the scale of SMOS data products (R ≈ 0.55), with simulated TB being better correlated than soil moisture retrievals. Following bias correction, TB and soil moisture retrievals at 800-m resolution had comparable performance to coarse-resolution SMOS data. Bias correction of TB was more reliable than soil moisture. These findings indicate that both TB and soil moisture retrievals can be assimilated in a land surface model at moderate-to-high resolution with a simple observation operator.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • One-Class Oriented Feature Selection and Classification of Heterogeneous
           Remote Sensing Images
    • Authors: Hossain; M.A.;Jia, X.;Benediktsson, J.A.;
      Pages: 1606 - 1612
      Abstract: Information extraction from spatial big data faces challenges in data relevancy analysis and heterogeneous data modeling. When the interested targets are more than one, the relevant analysis is often compromised. In this paper, a one-class oriented approach for effective feature selection and classification of remote sensing images is proposed. Mutual information (MI) is used as the feature selection criterion to cope with a wide range of data types. Then a cluster space (CS) representation is applied to model multimodal data and classifies each target class in turn. Hyperspectral and LiDAR data sets were used in the experiments. The test results demonstrate the advantage in terms of classification accuracies by focusing on one class at a time as compared to considering all classes simultaneously in classification.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • One-Class Classification of Remote Sensing Images Using Kernel Sparse
           Representation
    • Authors: Song; B.;Li, P.;Li, J.;Plaza, A.;
      Pages: 1613 - 1623
      Abstract: Sparse representations have been widely studied in remote sensing image analysis in recent years. In this paper, we develop a novel method for one-class classification (OCC) using a kernel sparse representation model for remotely sensed imagery. Training samples taken from the target class alone are used to build a learning dictionary for the sparse representation model, which is then optimized to produce a reconstruction residual. In the proposed model, a pixel is classified as the target class if the obtained reconstruction residual for the pixel is smaller than a given threshold; otherwise, the pixel is labeled as the outlier class. To improve the data separability between the target and outliner classes, the training samples taken from the target class are mapped into a high-dimensional feature space using a kernel function to build a learning dictionary for the kernel sparse representation model. OCC is then conducted in the mapped high-dimensional feature space using the reconstruction residual threshold, following the same principle as OCC in the original feature space. The proposed OCC method is evaluated and compared with several existing OCC methods in three different case studies. The experimental results indicate that the proposed method outperforms these existing methods, particularly when using a kernel sparse representation.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Relating Vegetation Dynamics to Climate Variables in Taiwan Using
           1982–2012 NDVI3g Data
    • Authors: Tsai; H.p.;Yang, M.-D.;
      Pages: 1624 - 1639
      Abstract: This research aims to improve our understanding of vegetation dynamics and associated climate variables in Taiwan by utilizing mean-variance analysis (MVA), relative directional persistence analysis, and Pearson's product moment correlation analysis on the Advanced Very High Resolution Radiometer (AVHRR)-derived NDVI3g data from 1982 to 2012. The results indicate a slightly increasing mean-normalized difference vegetation index (NDVI) value with a relatively higher variance during the 1990s and lower variance during the 2000s, which may be explained by the observed fluctuation in precipitation. Additionally, NDVI patterns are identified as increasing in the first half of the year and decreasing in the second half of the year. Spatially, decreasing patterns are observed in all regions except that the northern counties exhibit an increasing NDVI pattern supported by the observed increase in precipitation. Moreover, sunshine duration and temperature are positively correlated with NDVI, whereas precipitation and cloud amount exhibit a negative correlation with NDVI in Taiwan. In the context of global environmental change, this research highlights the utility of applying a combined spatial-temporal approach to remote sensing products. This is an approach with potential applications such as landscape management, conservation practice, and water resource management for policy makers and stakeholders in and beyond Taiwan.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • A Spatial Fuzzy Clustering Algorithm With Kernel Metric Based on Immune
           Clone for SAR Image Segmentation
    • Authors: Shang; R.;Tian, P.;Jiao, L.;Stolkin, R.;Feng, J.;Hou, B.;Zhang, X.;
      Pages: 1640 - 1652
      Abstract: The fuzzy c-means (FCM) clustering algorithm has been widely used in image segmentation. However, FCM exhibits poor robustness to noise, often leading to unsatisfactory segmentations on noisy images. Additionally, the FCM algorithm is sensitive to the choice of initial cluster centers. In order to solve these problems, this paper proposes clone kernel spatial FCM (CKS_FCM), which improves segmentation performance in several ways. First, in CKS_FCM, an immune clone algorithm is used to generate the initial cluster centers, which helps prevent the algorithm from converging on local optima. Second, CKS_FCM improves the robustness to noise by incorporating spatial information into the objective function of FCM. Third, CKS_FCM uses a non-Euclidean distance based on a kernels metric, instead of the Euclidean distance conventionally used in FCM, to enhance the segmentation accuracy (SA). We present experimental results on both real and synthetic SAR images, which suggest that the proposed method can generate higher accuracy, and obtain more robustness to noise, as compared against six state-of-the-art methods from the literatures.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Parallel Computation of Aerial Target Reflection of Background Infrared
           Radiation: Performance Comparison of OpenMP, OpenACC, and CUDA
           Implementations
    • Authors: Guo; X.;Wu, J.;Wu, Z.;Huang, B.;
      Pages: 1653 - 1662
      Abstract: The infrared (IR) signature of an aerial target due to the reflection of radiation from the Sun, the Earth's surface and atmosphere plays an important role in aerial target detection and tracking. As the background radiation from the Earth's surface, and atmosphere is distributed in the entire space and in a wide spectrum, it is time-consuming to obtain an aerial target's reflected radiation. This problem is suitable for parallel implementation to run on multicore CPU or many-core GPU because the reflection of background radiation incident from different directions in each spectral wavelength can be calculated in parallel. We consider three different parallel approaches: 1) CPU implementation using OpenMP (open multiprocessing); 2) GPU implementation using OpenACC (open accelerators); and 3) GPU implementation using CUDA (compute unified device architecture). An NVIDIA K20c GPU (with 2496 cores) and two Intel Xeon E5-2690 CPU (with 8 cores each) are used in our experiment. Compared to their single-threaded CPU counterpart, speedups obtained by OpenMP, OpenACC, and CUDA implementations are 15x, 140x, 426x, respectively. The result shows that GPU implementations are promising in our problem.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Integration of InSAR Analysis and Numerical Modeling for the Assessment of
           Ground Subsidence in the City of Lisbon, Portugal
    • Authors: Catalao; J.;Nico, G.;Lollino, P.;Conde, V.;Lorusso, G.;Silva, C.;
      Pages: 1663 - 1673
      Abstract: In this work, we exploit the integration of an advanced synthetic aperture radar (SAR) interferometry technique and the application of the finite-element method for the assessment and the interpretation of a localized subsidence phenomenon that took place within a specific area of Lisbon, Portugal. SAR images over the Lisbon city, covering different time intervals in the period of 1995-2010, were acquired and processed by means of the persistent scatterers (PSs) technique. Results clearly reveals a localized subsidence, limited to an area 2 km × 1.5 km wide, which has been confirmed by the leveling performed in 1976, 1996, and 2010. A physical interpretation of the observed ground deformations is provided based on the results of a finite-element model using stratigraphic data, in situ piezometric measurements, and geotechnical properties of the involved soils. The ground subsidence is interpreted as the consequence of a consolidation process affecting the central fine-grained soil layer, which in turn has been driven by water withdrawal from the existing aquifers. The change of the hydraulic boundary conditions was generated by the excavation works for the construction of underground lines and also by the reduction of rainfall water infiltration as an effect of the increase in ground surface impermeable areas due to urbanization. The consequent consolidation process of the compressible fine-grained soil layer is supposed to provide a reasonable explanation of the observed time series of ground displacement in the examined area.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Mapping Global Fossil Fuel Combustion CO2 Emissions at High
           Resolution by Integrating Nightlight, Population Density, and Traffic
           Network Data
    • Authors: Ou; J.;Liu, X.;Li, X.;Shi, X.;
      Pages: 1674 - 1684
      Abstract: Quantification of global fossil fuel carbon dioxide (CO2) emissions at fine spatial resolution is emerging as a critical need in climate change research and policy-making. Numerous studies have constructed CO2 emission inventories by using spatial proxies, such as radiance-calibrated nightlight and population density, to downscale national emission data into finer spatial scales. However, only using nightlight imagery and population density datasets cannot sufficiently explain the spatial characteristics of potential emission sources from transportation. In this study, we integrated nighttime imagery, population density, and traffic network data to estimate CO2 emission, and performed a linear regression model with corrective measure to create a high-resolution global grid of fossil fuel carbon emissions in 2010. The experimental results show that the model which considers all these factors (nighttime lights, population density, and traffic network data) exhibited a more reasonable distribution of CO2 emission than those involving only one or two factors. Besides, in contrast to previous studies on the actual statistical data of CO2 emissions at the level of subadministrative units of mainland China and the United States, the correlation coefficient of our inventory is significantly larger than those of the other two inventories, while both the mean absolute error and root-mean-squared error of our inventory are the smallest values among the three inventories. The inventory established in this study shows strong agreement with subnational-level CO2 emission statistics. The resulting dataset and corresponding methods would be of immediate use to global climate managers and policy-making communities.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Optimizing the Temporal Scale in the Assimilation of Remote Sensing and
           WOFOST Model for Dynamically Monitoring Heavy Metal Stress in Rice
    • Authors: Liu; F.;Liu, X.;Wu, L.;Xu, Z.;Gong, L.;
      Pages: 1685 - 1695
      Abstract: Obtaining precise information regarding the levels of heavy metal stress in crops is vital for food security. The assimilation of remote sensing into the World Food Study (WOFOST) model provides a method for achieving the spatial-temporal evaluation of crop growth status, while the optimization of the temporal scale in assimilation framework has rarely been considered. In this study, the temporal scale was optimized based on a wavelet transform of the leaf area index (LAI) curves. The accurate simulation of LAI laid the foundation for high precision. As the dry weight of rice roots (WRT) was demonstrated to be the most stress-sensitive indicator, the measured WRT values were assimilated into the improved WOFOST model to realize the dynamic simulation of LAI. Finally, four optimal time points were determined based on the extreme areas in the d4 wavelet coefficient, providing a reference for the selection of remote sensing images. The verification in the two sample plots indicated that the assimilation with optimized temporal scale could significantly improve the efficiency on the basis of guaranteeing the accuracy, shortening the run time of model operation by more than 30%. Based on the optimized temporal scale, the RS-WOFOST assimilation framework was driven for each pixel in the study areas, achieving the spatial-temporal evaluation of heavy metal stress in rice. This study suggests that the wavelet transform to LAI is applicable for optimizing the temporal scale in assimilation, providing a reference for the improvement of assimilation results under the premise of balancing accuracy and efficiency.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Integrating Landsat Imageries and Digital Elevation Models to Infer Water
           Level Change in Hoover Dam
    • Authors: Tseng; K.-H.;Shum, C.K.;Kim, J.-W.;Wang, X.;Zhu, K.;Cheng, X.;
      Pages: 1696 - 1709
      Abstract: The Thematic Mapper onboard Landsat 4, 5, and Enhanced Thematic Mapper Plus (TM/ETM+) onboard Landsat 7 have frequency bands (green and SWIR) to effectively measure water body extents and their changes via the Modified Normalized Difference Water Index (MNDWI). Here, we developed a technique, called the thematic imagery-altimetry system (TIAS), to infer the vertical water changes from MNDWI horizontal water extent changes by integrating long-term TM/ETM+ imageries with available digital elevation models (DEMs). The result is a technique to quantify water level changes of natural or artificial water bodies over two decades. Several DEMs were used to compute intersects with TM/ETM+ water extent time series to evaluate the robustness of the technique. These DEMs include: the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map version 2 (ASTER-GDEM2, at 1 arcsec resolution), the Shuttle Radar Topography Mission version 2 (SRTM C-band at 1 arcsec), and the Global Multiresolution Terrain Elevation Data (GMTED2010 at 7.5 arcsec). We demonstrated our technique near Hoover Dam (HD) in Lake Mead to quantify its respective decadal water level.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological
           Structures With the Ensemble Kalman Filter
    • Pages: 1710 - 1724
      Abstract: Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • The Effect of Vegetation on the Remotely Sensed Soil Thermal Inertia and a
           Two-Source Normalized Soil Thermal Inertia Model for Vegetated Surfaces
    • Pages: 1725 - 1735
      Abstract: The estimation of soil thermal inertia (STI) (P) on the vegetated surface is a challenging task due to the difficulty in acquiring soil temperature under vegetation. In most cases, mixed surface temperature (T) is used to replace soil temperature (Ts) to estimate P. Inevitably, errors are introduced because of the effect of vegetation. In this paper, on the basis of a simplified STI concept and an operational algorithm of surface temperature separation, the differences of STI estimated from Ts, vegetation temperature (Tv) and T were quantified. When there is large difference between T and Ts (as much as 10 K), the mean absolute percentage difference (MAPD) between STI estimated from Ts (STIS) and STI estimated from T (STIM) can reach 60%. A normalized STI (STIN) to account for the vegetated surface was proposed in terms of the linear mixing theory, which can be used to estimate the relative soil water (SW) content. Under the condition that the wilting point of the soil moisture and the saturated soil moisture are known for an area, SW content can then be calculated from STIN. The comparisons of the relationships between soil moisture from the advanced microwave scanning radiometer-earth observing system and STIS, STIV, STIM, and soil moisture estimated from STIN show that STIN is the best indicator of soil moisture, with the highest correlation coefficient (R2) of 0.64 and 0.75 for the two validation domains.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • Generalized Differential Morphological Profiles for Remote Sensing Image
           Classification
    • Pages: 1736 - 1751
      Abstract: Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF “out-of-bag” error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional WorldView-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
  • An Effective Emission Depth Model for Passive Microwave Remote Sensing
    • Pages: 1752 - 1760
      Abstract: Knowing the exact penetration depth for passive microwave will enable us to gain a better understanding of the characteristics of the targets. But now, there is only a penetration depth model that can calculate the specific depth while it is suitable for active microwaves. However, due to the completely different working mechanisms, the penetration depth model is most likely limited when applying to passive microwaves. Therefore, an effective emission depth model for passive microwaves was first proposed based on the radiative transfer theory, assuming that a homogenous double-layer (soil and atmosphere) medium exists between the passive microwave radiometer and a certain soil depth. With simulated data from the advanced integral equation model (AIEM) and subsequent analysis, the effective emission depth model was further simplified to make it more practical with a root-mean-squared error (RMSE) of 0.46 cm and a bias of 0.032 cm. For the simplified model, only the soil moisture content and the noise-equivalent differential temperature and center frequency of the radiometer are required. By comparing the penetration depth model and simplified effective emission depth model, a certain soil moisture content that makes both the models reveal the same results is found.
      PubDate: April 2016
      Issue No: Vol. 9, No. 4 (2016)
       
 
 
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