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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: 7)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 3)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Microelectronic Engineering     Open Access   (Followers: 12)
Advances in Power Electronics     Open Access   (Followers: 28)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 263)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 24)
Annals of Telecommunications     Hybrid Journal   (Followers: 8)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 8)
Archives of Electrical Engineering     Open Access   (Followers: 12)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 26)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 18)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
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   (Followers: 1)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 44)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 15)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 226)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 92)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 3)
Electronics     Open Access   (Followers: 77)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 9)
Electronics For You     Partially Free   (Followers: 78)
Electronics Letters     Hybrid Journal   (Followers: 27)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 46)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 4)
Energy Storage Materials     Full-text available via subscription   (Followers: 2)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 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: 10)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 182)
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 4)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 84)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 71)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 39)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal  
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 58)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 64)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 53)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 17)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 36)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 17)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 28)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 62)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 9)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 14)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 31)
IET Power Electronics     Hybrid Journal   (Followers: 38)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 18)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 10)
IETE Technical Review     Open Access   (Followers: 11)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)     Open Access   (Followers: 2)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 46)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 15)
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: 10)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 18)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 8)
International Journal of Antennas and Propagation     Open Access   (Followers: 11)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 3)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 6)
International Journal of Electronics & Data Communication     Open Access   (Followers: 11)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 13)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 11)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 7)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 10)
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: 19)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 9)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 3)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 6)
International Journal on Communication     Full-text available via subscription   (Followers: 14)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 9)
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: 10)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 4)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 20)
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: 7)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 7)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription   (Followers: 3)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 149)
Journal of Information and Telecommunication     Open Access  
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: 7)
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 Semiconductors     Full-text available via subscription   (Followers: 4)
Journal of Sensors     Open Access   (Followers: 25)
Journal of Signal and Information Processing     Open Access   (Followers: 9)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 13)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Majalah Ilmiah Teknologi Elektro : Journal of Electrical Technology     Open Access   (Followers: 2)
Metrology and Measurement Systems     Open Access   (Followers: 5)
Microelectronics and Solid State Electronics     Open Access   (Followers: 17)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 32)
Nanotechnology, Science and Applications     Open Access   (Followers: 6)
Nature Electronics     Hybrid Journal  
Networks: an International Journal     Hybrid Journal   (Followers: 6)
Open Journal of Antennas and Propagation     Open Access   (Followers: 8)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 14)
Paladyn, Journal of Behavioral Robotics     Open Access   (Followers: 1)
Problemy Peredachi Informatsii     Full-text available via subscription  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Advances in Communications and Networking Technology     Hybrid Journal   (Followers: 3)
Recent Advances in Electrical & Electronic Engineering     Hybrid Journal   (Followers: 10)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 51)
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: 73)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 12)
Solid-State Electronics     Hybrid Journal   (Followers: 8)
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: 8)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 6)
Visión Electrónica : algo más que un estado sólido     Open Access   (Followers: 1)
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Wireless Power Transfer     Full-text available via subscription   (Followers: 4)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 13)
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]   [51 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: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • IEEE Geoscience and Remote Sensing Societys
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Information for authors
    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Institutional listings
    • Abstract: Advertisements.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Foreword to the Special Issue on Quality Improvements of Remote Sensing
    • Authors: H. Shen;X. Jia;J. M. Bioucas-Dias;N. Dobigeon;Y. Cui;F. Pacifici;
      Pages: 687 - 690
      Abstract: Remote sensing data are often degraded by many issues that may include the failure of onboard hardware, signal downlink, atmospheric conditions, and overall quality/age of the sensors (for example, in terms of signal-noise ratio or sharpness).
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Denoising Very High Resolution Optical Remote Sensing Images: Application
           and Optimization of Nonlocal Bayes method
    • Authors: Antoine Masse;Sébastien Lefèvre;Renaud Binet;Stéphanie Artigues;Gwendoline Blanchet;Simon Baillarin;
      Pages: 691 - 700
      Abstract: Very high resolution optical remote sensing images (RSI) are often corrupted by noise. Among popular denoising methods in the state of the art, nonlocal Bayes (NLB) has led to successful results on real datasets, with high quality and reasonable computation time. However, its computation time remains prohibitive with respect to requirements of operational RSI pipelines, such as Pléiades one. In this paper, we tackle such an issue and introduce several optimizations aiming to significantly reduce the computation time required by NLB while keeping the best denoising quality (i.e., preserving edges, textures, and homogeneous areas). More precisely, our improvements consist of reducing multiple estimations of a same pixel with a masking technique and modifying the spatial extent of the similar patch search area (i.e., one of the main parts of nonlocal algorithms, such as NLB). We report several experiments and discuss optimal settings for these parameters, allowing a gain in computation time of 50% (resp. 15%) with optimized masking strategy (resp. spatial extent of the search area). When both contributions are combined, we achieve the same denoising quality as standard NLB while doubling the computation efficiency, the latter being increased fivefold if we accept a very small (lower than 0.1%) loss in quality.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Nonlocal Similarity Based Nonnegative Tucker Decomposition for
           Hyperspectral Image Denoising
    • Authors: Xiao Bai;Fan Xu;Lei Zhou;Yan Xing;Lu Bai;Jun Zhou;
      Pages: 701 - 712
      Abstract: Compared with color or grayscale images, hyperspectral images deliver more informative representation of ground objects and enhance the performance of many recognition and classification applications. However, hyperspectral images are normally corrupted by various types of noises, which have a serious impact on the subsequent image processing tasks. In this paper, we propose a novel hyperspectral image denoising method based on tucker decomposition to model the nonlocal similarity across the spatial domain and global similarity along the spectral domain. In this method, 3-D full band patches extracted from a hyperspectral image are grouped to form a third-order tensor by utilizing the nonlocal similarity in a proper window size. In this way, the task of image denoising is transformed into a high-order tensor approximation problem, which can be solved by nonnegative tucker decomposition. Instead of a traditional alternative least square based tucker decomposition, we propose a hierarchical least square based nonnegative tucker decomposition method to reduce the computational cost and improve the denoising effect. In addition, an iterative denoising strategy is adopted to achieve better denoising outcome in practice. Experimental results on three datasets show that the proposed method outperforms several state-of-the-art methods and significantly enhances the quality of the corrupted hyperspectral image.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and
           Global Spatial–Spectral Total Variation
    • Authors: Wei He;Hongyan Zhang;Huanfeng Shen;Liangpei Zhang;
      Pages: 713 - 729
      Abstract: Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial–spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial–spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial–spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial–spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Fast Hyperspectral Image Denoising and Inpainting Based on Low-Rank and
           Sparse Representations
    • Authors: Lina Zhuang;José M. Bioucas-Dias;
      Pages: 730 - 742
      Abstract: This paper introduces two very fast and competitive hyperspectral image (HSI) restoration algorithms: fast hyperspectral denoising (FastHyDe), a denoising algorithm able to cope with Gaussian and Poissonian noise, and fast hyperspectral inpainting (FastHyIn), an inpainting algorithm to restore HSIs where some observations from known pixels in some known bands are missing. FastHyDe and FastHyIn fully exploit extremely compact and sparse HSI representations linked with their low-rank and self-similarity characteristics. In a series of experiments with simulated and real data, the newly introduced FastHyDe and FastHyIn compete with the state-of-the-art methods, with much lower computational complexity.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Review on Recent Developments in Fully Polarimetric SAR Image
    • Authors: Xiaoshuang Ma;Penghai Wu;Yanlan Wu;Huanfeng Shen;
      Pages: 743 - 758
      Abstract: The use of synthetic aperture radar (SAR) technology with quad-polarization data requires efficient polarimetric SAR (PolSAR) speckle filtering algorithms. During the last three decades, many effective methods have been developed to reduce the speckle in PolSAR images, and recent studies have generally shown a trend developing from local single-point filtering to nonlocal patch-based or globally collaborative filtering. The main goals of this paper are to make a comprehensive review of the existing PolSAR despeckling algorithms and highlight the recent development trends. In the experimental part, the filtering results obtained with both simulated and real PolSAR images are deployed to compare the performance of some of the state-of-the-art despeckling algorithms, which shows that all of the selected filters have their individual strengths and weaknesses.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Subpixellic Methods for Sidelobes Suppression and Strong Targets
           Extraction in Single Look Complex SAR Images
    • Authors: Rémy Abergel;Loïc Denis;Saïd Ladjal;Florence Tupin;
      Pages: 759 - 776
      Abstract: Synthetic aperture radar (SAR) images display very high dynamic ranges. Man-made structures (like buildings or power towers) produce echoes that are several orders of magnitude stronger than echoes from diffusing areas (vegetated areas) or from smooth surfaces (e.g., roads). The impulse response of the SAR imaging system is, thus, clearly visible around the strongest targets: sidelobes spread over several pixels, masking the much weaker echoes from the background. To reduce the sidelobes of the impulse response, images are generally spectrally apodized, trading resolution for a reduction of the sidelobes. This apodization procedure (global or shift-variant) introduces spatial correlations in the speckle-dominated areas that complicates the design of estimation methods. This paper describes strategies to cancel sidelobes around point-like targets while preserving the spatial resolution and the statistics of speckle-dominated areas. An irregular sampling grid is built to compensate the subpixel shifts and turn cardinal sines into discrete Diracs. A statistically grounded approach for point-like target extraction is also introduced, thereby providing a decomposition of a single look complex image into two components: a speckle-dominated image and the point-like targets. This decomposition can be exploited to produce images with improved quality (full resolution and suppressed sidelobes) suitable both for visual inspection and further processing (multitemporal analysis, despeckling, interferometry).
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Methods to Remove the Border Noise From Sentinel-1 Synthetic Aperture
           Radar Data: Implications and Importance For Time-Series Analysis
    • Authors: Iftikhar Ali;Senmao Cao;Vahid Naeimi;Christoph Paulik;Wolfgang Wagner;
      Pages: 777 - 786
      Abstract: The Sentinel-1 GRD (ground range detected) Level-1 product generated by the Instrument Processing Facility of the European Space Agency has noise artifacts at the image borders, which are quite consistent at both left and right sides of the satellite's cross track and at the start and end of the data take along track. The Sentinel-1 border noise troubles the creation of clean and consistence time series of backscatter. Data quality control and management become very challenging tasks, when it comes to the large-scale data processing, both in terms of spatial coverage and data volume. In this paper, we evaluate three techniques for removing the Sentinel-1 border noise and compare the results with the existing “Sentinel-1 GRD Border Noise Removal” algorithm implemented in the Sentinel-1 toolbox of the Sentinel application platform.1 Validation and evaluation of the newly proposed algorithms was done using random samples containing 1500 Sentinel-1 scenes selected from a complete Sentinel-1 archive. The newly proposed approach has successfully achieved the required level of accuracy and solved the issue of time-series anomalies due to the border noise.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Performance Evaluation of TanDEM-X Quad-Polarization Products in Bistatic
    • Authors: José-Luis Bueso-Bello;Pau Prats-Iraola;Michele Martone;Jens Reimann;Ulrich Steinbrecher;Paola Rizzoli;
      Pages: 787 - 799
      Abstract: In the TanDEM-X mission, quad-polarization data (HH, HV, VH, and VV-polarization channels) can be acquired at an experimental basis by acquiring images in the dual-receive antenna (DRA) mode. This mode was activated during the so-called TanDEM-X science phase, from October 2014 up to January 2016, serving the science community with a unique dataset for the demonstration of new SAR techniques and applications. Quad-polarization data has been firstly acquired in pursuit monostatic mode and, secondly, in bistatic configuration as well. TanDEM-X is the first spaceborne mission which allows for the acquisition of quad-polarization data in bistatic formation, with across-track baselines varying up to 4 km at the Equator. The current work completes the one presented in [1] , where TanDEM-X quadpolarization data, acquired in pursuit monostatic mode only, was analyzed and recommendations were drawn, in order to optimize the acquisition parameters, aiming at improving the final data quality. Such recommendations were then taken into account for the acquisition of quad-polarization data in bistatic configuration, starting from April 2015, and the obtained results are presented in this paper. Investigations have been performed, aiming at monitoring the effective improvement in data quality. For example, we investigated the impact of different system parameters, such as noise equivalent sigma zero (NESZ) or processing bandwidth on the SAR performance, together with their influence on the interferometric SAR (InSAR) performance, assessed in terms of interferometric coherence and relative height error. Finally, we introduce and discuss an experimental acquisition mode, which allows to synthesize a quad-polarization product by combining two simultaneous dual-polarization acquisitions.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Novel Method for Removing Snow Melting-Induced Fluctuation in GIMMS
           NDVI3g Data for Vegetation Phenology Monitoring: A Case Study in Deciduous
           Forests of North America
    • Authors: Cong Wang;Jin Chen;Yanhong Tang;T. Andrew Black;Kai Zhu;
      Pages: 800 - 807
      Abstract: The normalized difference vegetation index (NDVI) has been widely used in recent decades to monitor vegetation phenology. However, interference from snow cover introduces a high degree of uncertainty in interpreting NDVI fluctuation, because snow melting increases NDVI value in a manner similar to vegetation growth, leading to false detection. In this study, we present a novel methodology to smooth out data noise caused by snow in the third generation NDVI dataset from Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g). This method is developed to replace small values with a pixel-specific snow-free background NDVI estimate, based on the assumption that the existence of snow decrease NDVI value and the patterns of NDVI fluctuation after snow melting and that after initiation of vegetation growth are different. Using the daily gross primary production (GPP) data of 111 site-years from FLUXNET in nine North American sites and the GIMMS NDVI3g dataset, we found that the green-up onset day (GUD) derived from raw NDVI is 42.2 days earlier than that of GPP, on average. This difference decreases to 4.7 days when applying the newly developed method. Additionally, the root mean square error and Spearman's correlation coefficient between NDVI-derived GUD and GPP-derived GUD are improved from 46.8 to 12.8 days and 0.22 to 0.64, respectively. Our results indicate that this method could effectively improve the ability to monitor the vegetation phenology by NDVI time series in areas with seasonal snow cover.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Thermal Disaggregation Model Based on Trapezoid Interpolation
    • Authors: Kai Liu;Hongbo Su;Xueke Li;Shaohui Chen;Renhua Zhang;Weimin Wang;Lijun Yang;Hong Liang;Yongmin Yang;
      Pages: 808 - 820
      Abstract: A trapezoid interpolation thermal disaggregation (TI_DisTrad) model was proposed in this study. This model can disaggregate coarse resolution land surface temperature (LST) to fine resolution LST based on fractional vegetation cover (FVC) versus LST space. The proposed model assumes that the quantitative relationships among the Bowen ratio, FVC and LST can work for the pixels inside the FVC-LST space at both coarser and finer resolutions. Pixels that were outside the FVC-LST space were addressed with a support vector machine regression. We evaluated the TI_DisTrad model over an agricultural region in central Iowa (USA) and an urban region in Beijing (China). The performance of the TI_DisTrad model was assessed by comparing results against those of five other popular benchmark models. The results show that the TI_DisTrad model was slightly superior to three of the benchmark models over the agricultural regions and achieved more accurate LST compared to two of the benchmark models over the urban region. When using two surface energy balance models (the one-source model and the two-source model), the estimated evapotranspiration (ET) from the TI_DisTrad disaggregated LST data was more accurate than the estimated ET from the disaggregated LST obtained using the other benchmark approaches, corresponding to an increase in average accuracy of the TI_DisTrad model.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural
    • Authors: Huihui Song;Qingshan Liu;Guojie Wang;Renlong Hang;Bo Huang;
      Pages: 821 - 829
      Abstract: We propose a novel spatiotemporal fusion method based on deep convolutional neural networks (CNNs) under the application background of massive remote sensing data. In the training stage, we build two five-layer CNNs to deal with the problems of complicated correspondence and large spatial resolution gaps between MODIS and Landsat images. Specifically, we first learn a nonlinear mapping CNN between MODIS and low-spatial-resolution (LSR) Landsat images and then learn a super-resolution CNN between LSR Landsat and original Landsat images. In the prediction stage, instead of directly taking the outputs of CNNs as the fusion result, we design a fusion model consisting of high-pass modulation and a weighting strategy to make full use of the information in prior images. Specifically, we first map the input MODIS images to transitional images via the learned nonlinear mapping CNN and further improve the transitional images to LSR Landsat images via the fusion model; then, via the learned SR CNN, the LSR Landsat images are supersolved to transitional images, which are further improved to Landsat images via the fusion model. Compared with the previous learning-based fusion methods, mainly referring to the sparse-representation-based methods, our CNNs-based spatiotemporal method has the following advantages: 1) automatically extracting effective image features; 2) learning an end-to-end mapping between MODIS and LSR Landsat images; and 3) generating more favorable fusion results. To examine the performance of the proposed fusion method, we conduct experiments on two representative Landsat–MODIS datasets by comparing with the sparse-representation-based spatiotemporal fusion model. The quantitative evaluations on all possible prediction dates and the comparison of fusion results on one key date in both visual effect and quantitative evaluations demonstrate that the proposed method can generate more accurate fusion results.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • An Improved Novel Nonlinear Algorithm of Area-Wide Near-Surface Air
           Temperature Retrieval
    • Authors: Jiang-Lin Qin;Xiu-Hao Yang;Ji-Tong Luo;He Fu;Xiu-Feng Lei;Jun Wei;Yuan-Rui Qin;Zi-Xin Yang;
      Pages: 830 - 844
      Abstract: Remote sensing air temperature mostly relies on linear algorithms that produce significantly variable results depending on various weather conditions. Recently, a novel nonlinear algorithm based on support vector machine (SVM) was reported with improved prediction accuracy by using multiple types of data including satellite and unmanned weather station, land coverage imagery, digital elevation model, astronomy, and calendar. To further improve the accuracy and consistence, this paper reports a selective arithmetic mean (SAM) approach for optimization of a previously reported SVM algorithm for area-wide near surface air temperature remote sensing using satellite and other types of data. Using Guangxi province as the study area, the results show that this new SAM approach significantly improved the overall retrieving quality over the previously reported simple arithmetic mean approach. The SAM approach has high tolerance to cloud, ground vegetation, and vertical and spatial spectrum variations, with superb prediction errors (absolute error, AE) and root mean square errors concentrated around 0.7 and 0.8 °C, respectively. The prediction error patterns with different atmosphere water content, enhanced vegetation index, and spatial spectrum were similar under all examined conditions. After SAM operations, the prediction error patterns showed a deep gap near a set error threshold ${boldsymbol delta} _{i}$ , especially near δ0 (δ0 ± 0.2) in every examined situation. SAM also produces significantly lower errors at AE ≥ δ0 ≥ 0. The SVM model with SAM optimization minimizes the shortcomings of the classical temperature remote sensing technologies and is suitable for area-wide retrieving under natural conditions. Four modeli-g principles are summarized for building superb models.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Comparative Analysis of SCATSat-1 Gridded Winds With Buoys, ASCAT, and
           ECMWF Winds in the Bay of Bengal
    • Authors: Samiran Mandal;Sourav Sil;Abhijit Shee;Debadatta Swain;P. C. Pandey;
      Pages: 845 - 851
      Abstract: This paper presents the first results on comparisons of Scatterometer Satellite-1 (SCATSat-1) derived wind datasets with the in situ, reanalysis as well as another operational scatterometer derived winds in the Bay of Bengal during the period November 2016–March 2017. The comparisons of daily gridded wind products of SCATSat-1 with buoys show good correlations (>0.83), higher skill scores (>0.92), and lower root mean square errors (RMSEs) of 0–2 m/s for wind speeds (WS) at the buoy locations. Similarly, the results corresponding to wind directions (WD) show higher correlations (>0.95), higher skill scores (>0.96), and relatively lower RMSEs (15–30°). Further, the intercomparisons of SCATSat-1 with Advanced Scatterometer and European Centre for Medium Range Weather Forecasts reanalysis winds show strong correlations for both WS (>0.85) and WD (>0.94). This paper also reports the capability of SCATSat-1 to capture three tropical cyclones Kyant, Vardah, and Mora during the period of study with the highest WS of 23.5 m/s.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Mapping Forest and Their Spatial–Temporal Changes From 2007 to 2015 in
           Tropical Hainan Island by Integrating ALOS/ALOS-2 L-Band SAR and Landsat
           Optical Images
    • Authors: Bangqian Chen;Xiangming Xiao;Huichun Ye;Jun Ma;Russell Doughty;Xiangping Li;Bin Zhao;Zhixiang Wu;Rui Sun;Jinwei Dong;Yuanwei Qin;Guishui Xie;
      Pages: 852 - 867
      Abstract: Accurately monitoring forest dynamics in the tropical regions is essential for ecological studies and forest management. In this study, images from phase-array L-band synthetic aperture radar (PALSAR), PALSAR-2, and Landsat in 2006–2010 and 2015 were combined to identify tropical forest dynamics on Hainan Island, China. Annual forest maps were first mapped from PALSAR and PALSAR-2 images using structural metrics. Those pixels with a high biomass of sugarcane or banana, which are widely distributed in the tropics and subtropics and have similar structural metrics as forests, were excluded from the SAR-based forest maps by using phenological metrics from time series Landsat imagery. The optical–SAR-based forest maps in 2010 and 2015 had high overall accuracies (OA) of 92–97% when validated with ground reference data. The resultant forest map in 2010 shows good spatial agreement with public optical-based forest maps (OA = 88–90%), and the annual forest maps (2007–2010) were spatiotemporally consistent and more accurate than the PALSAR-based forest map from the Japan Aerospace Exploration Agency (OA = 82% in 2010). The areas of forest gain, loss, and net change on Hainan Island from 2007 to 2015 were 415 000 ha (+2.17% yr–1), 179 000 ha (–0.94% yr –1), and 236 000 ha (+1.23% yr–1), respectively. About 95% of forest gain and loss occurred in those areas with an elevation less than 400 m, where deciduous rubber, eucalyptus plantations, and urbanization expanded rapidly. This study demonstrates the potential of-PALSAR/PALSAR-2/Landsat image fusion for monitoring annual forest dynamics in the tropical regions.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Estimating Tree Crown Area and Aboveground Biomass in Miombo Woodlands
           From High-Resolution RGB-Only Imagery
    • Authors: Herbet Tichaona Mareya;Paradzayi Tagwireyi;Henry Ndaimani;Tawanda Winmore Gara;David Gwenzi;
      Pages: 868 - 875
      Abstract: Quantification of tree canopy area and aboveground biomass is essential for monitoring ecosystems’ ecological functionalities, e.g., carbon sequestration and habitat provision. Miombo woodlands are vastly existent in developing countries that often lack resources to acquire LiDAR data or high spatiospectral resolution remote sensing data that have been proven to accurately estimate these structural attributes. This study explored the utility of freely available (via Google Maps) high (1-m) resolution red, green, and blue (RGB) satellite imagery in combination with object-based image analysis (OBIA) for estimating tree canopy area and aboveground biomass in Miombo woodlands. We randomly established 41 225-m2 plots in Mukuvisi Woodland, Zimbabwe, and used RGB data with OBIA to estimate tree canopy area in those plots. We also field measured the height, canopy area, and trunk diameter at breast height of all trees that fell in those plots, then used the field data and a published allometric equation to estimate aboveground tree biomass (AGB). OBIA classification accuracy was high (Jaccard similarity index = 0.96). Data analysis showed significant positive linear relationship between AGB and field-measured canopy area $(R^{2} = {{0.87}}, p < {{0.003}})$, and significant exponential relationships between: 1) OBIA-derived canopy area and AGB $(R^{2} = {{0.56}}, p < {{0.0001}})$; and 2) field-measured canopy area and OBIA-derived canopy area $(R^{2} = {{0.63}}, p < {{0.0001}})$ , and no significant differences $(t = {{19.67}}, df = {{78}}, p = {{0.28}})$ between field-measured canopy are- ( $bar{ times } = 187.11,{{rm{m}}^2},sigma = 127.03$) and OBIA-derived canopy area ($bar{ times } = 163.00,{{rm{m}}^2},sigma = 50.08$). We conclude that RGB data with OBIA are suitable for estimating tree canopy area in Miombo woodlands for various low-accuracy purposes (e.g., biomass estimation).
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks
    • Authors: Zhun Fan;Jiewei Lu;Maoguo Gong;Honghui Xie;Erik D. Goodman;
      Pages: 876 - 887
      Abstract: Tobacco plant detection plays an important role in the management of tobacco planting. In this paper, a new algorithm based on deep neural networks is proposed to detect tobacco plants in images captured by unmanned aerial vehicles (UAVs) (called UAV images). These UAV images are characterized by a very high spatial resolution (35 $text{mm}$), and consequently contain an extremely high level of detail for the development of automatic detection algorithms. The proposed algorithm consists of three stages. In the first stage, a number of candidate tobacco plant regions are extracted from UAV images with the morphological operations and watershed segmentation. Each candidate region contains a tobacco plant or a nontobacco plant. In the second stage, a deep convolutional neural network is built and trained with the purpose of classifying the candidate regions as tobacco plant regions or nontobacco plant regions. In the third stage, postprocessing is performed to further remove the nontobacco plant regions. The proposed algorithm is evaluated on a UAV image dataset. The experimental results show that the proposed algorithm performs well on the detection of tobacco plants in UAV images.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Evaluation of the Tau–Omega Model for Passive Microwave Soil Moisture
           Retrieval Using SMAPEx Datasets
    • Authors: Ying Gao;Jeffrey P. Walker;Nan Ye;Rocco Panciera;Alessandra Monerris;Dongryeol Ryu;Christoph Rüdiger;Thomas J. Jackson;
      Pages: 888 - 895
      Abstract: The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau–omega model from 1) the soil moisture active passive (SMAP) algorithm theoretical basis document (ATBD) for global condition and 2) calibrated parameters from the National Airborne Field Experiment (NAFE’05) for Australian conditions, with special focus on the vegetation parameter b and roughness parameter $H_{R}$. This study uses airborne L-band data and field observations from the SMAP experiments conducted in south-eastern Australia. Results show that the accuracy with the proposed parameterizations from SMAP ATBD was satisfactory at 100-m spatial resolution for maize (0.07 m3/m3) and pasture (0.07 m3/m3 ), while it decreased to 0.19 m3/m3 for wheat. Calibrated parameters from the NAFE’05 did not provide better results, with the accuracy of wheat degrading to 0.23 m3/m3. After a comprehensive site-specific calibration and validation at 100-m spatial resolution, this result was improved to 0.10 m3/m3. Further calibration and validation were performed at 1-km resolution against intensive ground sampling and at 3-km against in situ monitoring stations. Results showed an accuracy over grassland and cropland of 0.04 m3/m3 and 0.05 m3/m3 , respectively. This study also suggests that the paramet-rs from SMAP ATBD show an underestimation of soil moisture, with the roughness parameter $H_{R}$ being too low for south-eastern Australian condition. Therefore, a new set of b and $H_{R}$ parameters for ten different land cover types was proposed in this study.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial
           Information for SAR Image Segmentation
    • Authors: Ling Wan;Tao Zhang;Yuming Xiang;Hongjian You;
      Pages: 896 - 906
      Abstract: The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM$_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM$_$BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Adaptive Multilooking Based on Complex Patch for Multitemporal
    • Authors: Yingjie Wang;Yunkai Deng;Robert Wang;Jili Wang;
      Pages: 907 - 918
      Abstract: Multilooking is a key step in interferometric processing, especially in so-called coherent stacking interferometry approaches. In the past, multilooking algorithms were mainly implemented in the spatial domain on single interferometric pairs. With continuous development in repeat pass capabilities, multitemporal coherent synthetic aperture radar (SAR) images are now generally acquired more easily, thus providing the possibility to exploit also the temporal signature for multilooking. In this field, the possibility to carry out adaptive multilooking is fundamental for the improvement of interferometric processing. A basic similarity test has been introduced in the SqueeSAR approach, namely the Kolmogorov–Smirnov test. Furthermore, similarity tests have been discussed in terms of real-valued data vector, so only amplitude information can be utilized: The influence of the phase signal is typically ignored. To fully exploit the complex information acquired by coherent SAR systems, this paper proposes an adaptive multilooking algorithm based on complex patch (AMCP). The complex signal, which is fundamental in interferometric systems, is here exploited for the derivation of a new patch selection method. The AMCP algorithm can be applicable to all multitemporal techniques that need filtering, including the InSAR stacks of single main image and multi main images. Experiments on simulated data and real data validate that the proposed algorithm has the highlighting major advantages in improving measurement precision compared with traditional methods.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Optimum Baseline of a Single-Pass In-SAR System to Generate the Best DEM
           in Tidal Flats
    • Authors: Changhyun Choi;Duk-jin Kim;
      Pages: 919 - 929
      Abstract: Continuous monitoring of topographic heights and changes in tidal flats is challenging, as it is generally difficult to observe topographic changes from on-site measurements or remote sensing techniques with high resolution and high accuracy. In this regard, an interferometric synthetic aperture radar (In-SAR) can be an effective tool to generate precise digital elevation models (DEMs) and detect large-scale topographic changes. Nevertheless, utilizing the In-SAR to detect topographic changes in tidal flats is not practical because the average slope of tidal flats is usually less than 5°, and the overall spatial and temporal variations of height are not significant. Therefore, the accuracy of In-SAR DEMs must be high to detect meaningful topographic changes. In order to minimize the error of In-SAR DEMs, height of ambiguity and random phase deviation of interferograms should be taken into account. These two factors are related to incidence angle and baseline. We simulated topographic error levels in tidal flats for a single-pass In-SAR system such as TanDEM-X. Phase error of interferograms was derived using the relationship between In-SAR coherence and the probability density function of phase deviation. Signal-to-noise ratio and geometric decorrelation were formulated by the function of baseline, incidence angle, and surface slope. The simulation results show that the height error of the DEM was minimized to lower than 15 cm when the baseline was 1500 m with an incidence angle of 29° in the TanDEM-X system. Finally, the validation of simulation results was carried out by comparing them with TanDEM-X DEM accuracies in tidal flats.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Harvest Monitoring of Kenyan Tea Plantations With X-Band SAR
    • Authors: Boris Snapir;Toby W. Waine;Ronald Corstanje;Sally Redfern;Jacquie De Silva;Charles Kirui;
      Pages: 930 - 938
      Abstract: Tea is an important cash crop in Kenya, grown in a climatically restricted geographic area where climatic variability is starting to affect yield productivity levels. This paper assesses the feasibility of monitoring tea growth between, but also within fields, using X-band COSMO-SkyMed SAR images (five images at VV polarization and five images at HH polarization). We detect the harvested and nonharvested areas for each field, based on the loss of interferometric coherence between two images, with an accuracy of 52% at VV polarization and 74% at HH polarization. We then implement a normalization method to isolate the scattering component related to shoot growth and eliminate the effects of moisture and local incidence angle. After normalization, we analyze the difference in backscatter between harvested and nonharvested areas. At HH polarization, our backscatter normalization reveals a small decrease ($sim0.1$ dB) in HH backscatter after harvest. However, this decrease is too small for monitoring shoot growth. The decrease is not clear at VV polarization. This is attributed to the predominantly horizontal orientation of the harvested leaves.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Deceptive SAR Jamming Based on 1-bit Sampling and Time-Varying Thresholds
    • Authors: Bo Zhao;Lei Huang;Jian Li;Maliang Liu;Jinwei Wang;
      Pages: 939 - 950
      Abstract: This paper addresses the issue of deceptive jamming against synthetic aperture radar (SAR) by using 1-bit sampling and time-varying threshold (TVT). With 1-bit intercepted SAR signal, the multipliers involved in a convolution is replaced by xnor gates, which considerably simplify the jamming signal generation. Moreover, the TVT is used for 1-bit quantization before retransmission to retain the relative amplitude information of the jamming signal. As a result, the proposed deceptive jamming schemes are superior to their conventional counterpart in terms of realization. Effects of harmonics and oversampling are analyzed to evaluate the performance degradations caused by the 1-bit sampling and TVT. Simulation results are provided to confirm the validity of the proposed schemes.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Piecewise Linear Strategy of Target Detection for
           Multispectral/Hyperspectral Image
    • Authors: Xiurui Geng;Weitun Yang;Luyan Ji;Cheng Ling;Suixin Yang;
      Pages: 951 - 961
      Abstract: The linear operator has been widely used to detect targets of interest in multispectral/hyperspectral images, and is usually able to achieve good performance when the target is linearly separable from the background. However, when dealing with a complex scene, it is hard to find a single projection direction, along which the target can be well distinguished from all the background objects. Therefore, we propose a piecewise linear strategy (PLS) for target detection, which is based on the assumption that the desired target is generally linearly separable from each background object. PLS first divides the whole background into several partitions, and then detects the individual target for each partition by using a commonly used linear detector. Experiments with simulated and real-world multispectral/hyperspectral images show that PLS can acquire a nonlinear detection result and can outperform state-of-the-art target detection operators.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Domain-Adapted Convolutional Networks for Satellite Image Classification:
           A Large-Scale Interactive Learning Workflow
    • Authors: Dalton Lunga;Hsiuhan Lexie Yang;Andrew Reith;Jeanette Weaver;Jiangye Yuan;Budhendra Bhaduri;
      Pages: 962 - 977
      Abstract: Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. This paper investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • A Multiscale and Multidepth Convolutional Neural Network for Remote
           Sensing Imagery Pan-Sharpening
    • Authors: Qiangqiang Yuan;Yancong Wei;Xiangchao Meng;Huanfeng Shen;Liangpei Zhang;
      Pages: 978 - 989
      Abstract: Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multispectral (MS) images. As the transformation from low spatial resolution MS image to high-resolution MS image is complex and highly nonlinear, inspired by the powerful representation for nonlinear relationships of deep neural networks, we introduce multiscale feature extraction and residual learning into the basic convolutional neural network (CNN) architecture and propose the multiscale and multidepth CNN for the pan-sharpening of remote sensing imagery. Both the quantitative assessment results and the visual assessment confirm that the proposed network yields high-resolution MS images that are superior to the images produced by the compared state-of-the-art methods.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Joint Clusters and Iterative Graph Cuts for ALS Point Cloud Filtering
    • Authors: Huan Ni;Xiangguo Lin;Jixian Zhang;Dong Chen;Jiju Peethambaran;
      Pages: 990 - 1004
      Abstract: A novel method that combines joint clusters and iterative graph cuts for ALS point cloud filtering is proposed in this paper. The method first extracts clusters of points from the raw point cloud, and then classifies ground points at the cluster level. There are four main steps, i.e., two-step point cloud clustering, critical point extraction, initial terrain determination, and terrain densification based on iterative graph cuts. Smooth clusters, rough clusters, and scattered points are extracted by the two-step clustering to depict the raw point cloud, which reduces the complexity of raw data and the judgment difficulty in the subsequent procedures. Critical points of each cluster are extracted, and the initial terrain is determined among the smooth clusters. Using the initial terrain and critical points, iterative graph cuts is performed to segment ground and nonground points at the cluster level. Experiments on ISPRS dataset with a low point density and Utah dataset with a moderate point density show that our approach provides a satisfactory trade off between Type I and Type II errors. Moreover, our method significantly outperforms progressive TIN densification based filters, and successfully controls Type II errors.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • High-Resolution Velocity Analysis Method Using the ℓ-1 Norm Regularized
           Least-Squares Method for Pavement Inspection
    • Authors: Li Yi;Lilong Zou;Kazunori Takahashi;Motoyuki Sato;
      Pages: 1005 - 1015
      Abstract: We propose a high-resolution velocity analysis method to estimate the electromagnetic wave propagation velocity in subsurface medium. The estimation is achieved by applying the ℓ-1 norm regularized least-squares method to the conventional common-midpoint (CMP) velocity analysis algorithm. The proposed method can provide not only higher resolution than the conventional velocity analysis method, but can also be applied with a coarse sampling array system, such as our array ground penetrating radar YAKUMO, which returns eight CMP traces within a two meter width. The main purpose of this approach is for precise pavement inspection at shallow depths. We applied this method to both a simulated dataset and real data acquired by YAKUMO at a model airport taxiway to detect the slight velocity changes caused by millimeter-thin cracks filled with air or water within the 15 cm-thick asphalt pavement. In both cases slight velocity changes of about 0.005 m/ns can be detected, and the difference between air- and water-filled cracks can be distinguished. Also, this method is applied to a data acquired at airport taxi-way, the damaged parts are detected successfully and shows good agreement with the corning results. The results indicate that the proposed method is effective for pavement inspection, especially in the presence of thin cracks that cannot be seen directly with the reflected signal.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
  • Become a published author in 4 to 6 weeks
    • Pages: 1016 - 1016
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: March 2018
      Issue No: Vol. 11, No. 3 (2018)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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