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  Subjects -> ELECTRONICS (Total: 156 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 5)
Advances in Electrical and Electronic Engineering     Open Access  
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
Advances in Microelectronic Engineering     Open Access   (Followers: 9)
Advances in Power Electronics     Open Access   (Followers: 19)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 189)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 22)
Annals of Telecommunications     Hybrid Journal   (Followers: 7)
Archives of Electrical Engineering     Open Access   (Followers: 11)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 24)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 30)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 39)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 13)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 88)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 71)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 56)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 60)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 40)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 3)
Energy Storage Materials     Full-text available via subscription   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 12)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Foundations and Trends® in Communications and Information Theory     Full-text available via subscription   (Followers: 7)
Foundations and Trends® in Signal Processing     Full-text available via subscription   (Followers: 5)
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal   (Followers: 1)
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 125)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 52)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 40)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 27)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 7)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 45)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 39)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 46)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 14)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 30)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 11)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 21)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 49)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 6)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 12)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 14)
IET Power Electronics     Hybrid Journal   (Followers: 24)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 16)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 9)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 29)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 15)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 7)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 17)
International Journal of Antennas and Propagation     Open Access   (Followers: 9)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 4)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 13)
International Journal of Electronics     Hybrid Journal   (Followers: 2)
International Journal of Electronics & Data Communication     Open Access   (Followers: 8)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 11)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 1)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 8)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 12)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 7)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 4)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 5)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 7)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 2)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 4)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 7)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 15)
Journal of Electrical Bioimpedance     Open Access   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 7)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 6)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 5)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 3)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 119)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 6)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
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: 30)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 7)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 19)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 13)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 32)
Nanotechnology, Science and Applications     Open Access   (Followers: 3)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 4)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 13)
Paladyn, Journal of Behavioral Robotics     Open Access  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 5)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 5)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 4)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 47)
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: 5)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 55)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 9)
Solid-State Electronics     Hybrid Journal   (Followers: 6)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 1)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 5)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 5)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 5)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 11)
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]   [47 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Provides a listing of the editorial board, current staff, committee members and society officers.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Information for Authors
    • Abstract: Presents institutional listings relating to this publication.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Institutional Listings
    • Abstract: Presents institutional listings relating to this publication.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • A Deep Collaborative Computing Based SAR Raw Data Simulation on Multiple
           CPU/GPU Platform
    • Authors: Fan Zhang;Chen Hu;Wei Li;Wei Hu;Pengbo Wang;Heng-Chao Li;
      Pages: 387 - 399
      Abstract: The outstanding computing ability of a graphics processing unit (GPU) brings new vitality to the typical computing intensive issue, so does the synthetic aperture radar (SAR) raw data simulation, which is a fundamental problem in SAR system design and imaging research. However, the computing power of a CPU was underestimated, and the tunings for a CPU-based method were missing in the previous works. Meanwhile, the collaborative computing of multiple CPUs/GPUs was not exploited thoroughly. In this paper, we propose a deep multiple CPU/GPU collaborative computing framework for time-domain SAR raw data simulation, which not only introduces the advanced vector extension (AVX) method to improve the computing efficiency of a multicore single instruction multiple data CPU, but also achieves a satisfactory speedup in the CPU/GPU collaborative simulation by fine-grained task partitioning and scheduling. In addition, an irregular reduction based SAR coherent accumulation approach is proposed to eliminate the memory access conflict, which is the most difficult issue in the GPU-based raw data simulation. Experimental results show that the multicore vector extension method greatly improves the computing power of a CPU-based method through about 70× speedup, thereby outperforming the single GPU simulation. Correspondingly, compared with the baseline sequential CPU approach, the multiple CPU/GPU collaborative simulation achieves up to 250× speedup. Furthermore, the irregular reduction based atomic-free optimization boosts the performance of the single GPU method by 20% acceleration. These results prove that the deep multiple CPU/GPU collaborative method is promising, especially for the case of huge volume raw data simulation with a wide swath and high resolution.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • The Reconnection of Contour Lines from Scanned Color Images of
           Topographical Maps Based on GPU Implementation
    • Authors: Jianfeng Song;Panfeng Wang;Qiguang Miao;Ruyi Liu;Bormin Huang;
      Pages: 400 - 408
      Abstract: This paper presents a method for the reconnection of contour lines from scanned color images of topographical maps based on graphics processing unit (GPU) implementation. The extraction of contour lines, which are shown with brown color on USGS maps, is a difficult process due to aliasing and false colors induced by the scanning process and due to closely spaced and intersecting/overlapping features inherent to the map. First, an effective method is presented for contour line reconnection from scanned topographical maps based on CPU. This method considers both the distance and direction between the two broken points of the contour lines. It gets better performance and has high connection rate, but the time complexity of the algorithm is nonlinear with the increasing size of topographical map. Second, the advantage of the massively parallel computing capability of GPU with the compute unified device architecture is taken to improve the algorithm. Finally, a better performance has been achieved based on the open source computer vision library. The experimental results show that the GPU implementation with loop-based patterns achieves a speedup of 1360× and the identical result compared with the implementation on CPU.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • A New Cloud Computing Architecture for the Classification of Remote
           Sensing Data
    • Authors: Victor Andres Ayma Quirita;Gilson Alexandre Ostwald Pedro da Costa;Patrick Nigri Happ;Raul Queiroz Feitosa;Rodrigo da Silva Ferreira;Dário Augusto Borges Oliveira;Antonio Plaza;
      Pages: 409 - 416
      Abstract: This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Comparison Between Two Attenuation Models and Precipitation Evaluation
           With Ground Validation
    • Authors: Denis Alexander Poffo;Jorge Nicolás Saffe;Giorgio Mario Caranti;Raúl Alberto Comes;Andres Rodriguez;
      Pages: 417 - 427
      Abstract: In this paper, we present an algorithm to correct the horizontal reflectivity factor Zh for attenuation by absorption plus scattering of a polarimetric radar located at the Experimental Station of INTA, in Oro Verde, Entre Rios, Argentina. Data correspond to two storms which occurred on November 18, 2009. The correction obtained was compared with models from the literature. Results show good agreement for regions with reflectivity factor lower than 55 dBZ while for regions that go over this limit, the corrections are different. Comparisons with rain gauges indicate the need of more statistics to improve the correlation.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • New Snow Water Equivalent Processing System With Improved Resolution Over
           Europe and its Applications in Hydrology
    • Authors: Matias Takala;Jaakko Ikonen;Kari Luojus;Juha Lemmetyinen;Sari Metsämäki;Juval Cohen;Ali Nadir Arslan;Jouni Pulliainen;
      Pages: 428 - 436
      Abstract: The presence and amount of snow, given in terms of snow water equivalent (SWE), is an essential physical characteristic influencing climate and hydrological processes. For the recent decades, remote sensing has proven to be a valuable tool for deriving regional and global scale information on SWE. However, determining SWE reliably from remote sensing data for many local-scale applications remains a challenge. Microwave radiometers are currently the best option to determine SWE since they respond to snow depth and density. Further, weather phenomena and solar illumination are not of concern. However, for some purposes the typical spatial resolution of space-borne radiometers (in the order of tens of kilometers) is not sufficient. In this study, the spatial resolution of existing operational SWE products (GlobSnow and H-SAF product portfolios) is improved by performing assimilation of ground truth observations of snow depth and space borne derived SWE estimates in a resolution grid of 0.05° × 0.05° (approximately 5 km × 5 km). Some modifications to the SWE algorithm and the applied auxiliary data (such as an improved forest stem volume map) are introduced. We will present how the improved resolution enhances spatial details in the retrieved SWE, while the validation results show that in terms of accuracy, the new product is on similar level than the existing operational products. Finally, the gained new SWE estimates are ingested into the HOPS hydrological model in the Ounasjoki river basin. The results indicate that simulation of snow melt driven river discharge can be improved by ingesting the retrieved SWE data into a hydrological model.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • A Novel Spectral-Unmixing-Based Green Algae Area Estimation Method for
           GOCI Data
    • Authors: Bin Pan;Zhenwei Shi;Zhenyu An;Zhiguo Jiang;Yi Ma;
      Pages: 437 - 449
      Abstract: Geostationary Ocean Color Imager (GOCI) data have been widely used in the detection and area estimation of green algae blooms. However, due to the low spatial resolution of GOCI data, pixels in an image are usually “mixed,” which means that the region a pixel covers may include many different materials. Traditional index-based methods can detect whether there are green algal blooms in each pixel, whereas it is still challenging to determine the proportion that green algae blooms occupy in a pixel. In this paper, we propose a novel subpixel-level area estimation method for green algae blooms based on spectral unmixing, which can not only detect the existence of green algae but also determine their proportion in each pixel. A fast endmember extraction method is proposed to automatically calculate the endmember spectral matrix, and the abundance map of green algae which could be regarded as the area estimation is obtained by nonnegatively constrained least squares. This new fast endmembers extraction technique outperforms the classical N-FINDR method by applying two models: candidates location and distance-based vertices determination. In the first model, we propose a medium-distance-based candidates location strategy, which could reduce the searching space during vertices selection. In the second model, we replace the simplex volume measure with a more simple distance measure, thus complex matrix determinant calculation is avoided. We have theoretically proven the equivalency of volume and distance measure. Experiments on GOCI data and synthetic data demonstrate the superiority of the proposed method compared with some state-of-art approaches.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Satellite-Based Estimation of Column-Integrated Algal Biomass in Nonalgae
           Bloom Conditions: A Case Study of Lake Chaohu, China
    • Authors: Jing Li;Yuchao Zhang;Ronghua Ma;Hongtao Duan;Steven Loiselle;Kun Xue;Qichun Liang;
      Pages: 450 - 462
      Abstract: In shallow lakes, algal biomass is a fundamental indicator of eutrophication status. However, the vertical movement of phytoplankton within the water column can complicate the determination of total phytoplankton biomass using remotely sensed data of surface conditions. In this study, we develop, validate, and apply a new approach to use remotely sensed reflectance to estimate the variability of total algal biomass in shallow eutrophic lakes. Using the baseline normalized difference bloom index together with hydrological and bathymetric data, we determine the spatial and temporal dynamics of the total algal biomass in Lake Chaohu, a large shallow lake in eastern China under the nonalgae bloom conditions. Over an eleven-year period (2003-2013), the total phytoplankton biomass was highly variable, more than doubling between 2006 and 2007, from 19.95t to 39.50t. The seasonal decomposition of biomass dynamics indicated the highest biomass production occurred in June, while the lowest occurred in April. The estimates of total phytoplankton biomass were both consistent with in situ measurements and consistent for observations made on the same day and on consecutive days. The improved stability and reliability of total biomass estimations provided more complete information about lake conditions with respect to surface concentrations. This has implications for both management and modeling.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Oil Slicks Detection From Polarimetric Data Using Stochastic Distances
           Between Complex Wishart Distributions
    • Authors: Patrícia Carneiro Genovez;Corina C. Freitas;Sidnei J. S. Sant'Anna;Cristina Maria Bentz;João A. Lorenzzetti;
      Pages: 463 - 477
      Abstract: Polarimetric synthetic aperture radars (PolSAR) have been used to detect oil slicks at the sea surface. Different techniques to extract information from polarimetric data, using an adequate statistical distribution are currently available. A region-based classifier for PolSAR data - named PolClass - uses a supervised approach to compare stochastic distances between scaled complex Wishart distributions and hypothesis tests to associate confidence levels into the classification results. In this paper, the integrated use of these distances together with the uncertainty maps is applied for the first time to detect oil slicks. A quad-pol Radarsat-2 data, acquired during one open-water controlled exercise, was used to perform this test. The PolClass achieved similar overall accuracies for four stochastic distances, reaching 96.54% of global accuracy, the best result obtained by the Hellinger distance. A comparison between the full- and dual-pol matrices indicated that the results obtained with the VV-HH-HV, HH-HV, and VV-HV polarizations are statistically equivalent, but different from that obtained using the HH-VV. Therefore, the exclusion of the HV channel affected the detection of only mineral oils. The classifier demonstrated the potential to detect the three types of oils released, being more effective in detecting biogenic oils rather than mineral oils. The uncertainty levels increase from the center to the border of the mineral oil slicks, indicating the presence of transition regions, possibly related to different weathering mechanisms. The proposed approach will contribute to the understanding of where different physical and chemical processes may be acting, associating confidence levels to the classification results.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Utilizing Self-Regularized Regressive Models to Downscale Microwave
           Brightness Temperatures for Agricultural Land Covers in the SMAPVEX-12
           Region
    • Authors: Subit Chakrabarti;Jasmeet Judge;Anand Rangarajan;Sanjay Ranka;
      Pages: 478 - 488
      Abstract: A novel algorithm is developed to downscale microwave brightness temperatures (TB), obtained at satellite scales of 10-40 to ≤1km, meaningful for agricultural applications. Downscaling TB directly bypasses the errors induced by inverse modeling encountered while downscaling satellite-based soil moisture products. This algorithm is based upon self-regularized regressive models (SRRM) and uses higher order correlations between auxiliary variables, such as precipitation (PPT), land cover, leaf area index, and land surface temperature, and horizontally polarized TB observations. It includes information-theoretic clustering based on auxiliary variables to identify areas of similarity, followed by kernel regression that produces downscaled TB. The algorithm was evaluated using a multiscale synthetic dataset over North Central Florida for one year, including two growing seasons of corn and one growing season of cotton. Compared to the true TB, the downscaled TB had a root-mean-square error (RMSE) of 5.76 K with standard deviation (SD) of 2.8 K during the growing seasons and an RMSE of 1.2 K with an SD of 0.9 K during nonvegetated. The SRRM algorithm effectively captured the variability in TB at 1 km through the auxiliary variables. This algorithm was implemented to downscale SMOS observations available for five days during the SMAPVEX-12 experiment. Spatially averaged rootmean-square difference (RMSD) between the downscaled TB and the airborne TB observations from the airborne passive-active Lband sensor was 6.2 K, with Kullback-Leibler divergences of up to 0.91. For the SMAPVEX-12 dataset, better downscaling results are obtained for days when there was no PPT due to regional biases in the remotely sensed PPT from the NASA Tropical Measurement Mission. The RMSDs were lower when in-situ PPT data were used.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Application of Triple Collocation in Ground-Based Validation of Soil
           Moisture Active/Passive (SMAP) Level 2 Data Products
    • Authors: Fan Chen;Wade T. Crow;Andreas Colliander;Michael H. Cosh;Thomas J. Jackson;Rajat Bindlish;Rolf H. Reichle;Steven K. Chan;David D. Bosch;Patrick J. Starks;David C. Goodrich;Mark S. Seyfried;
      Pages: 489 - 502
      Abstract: The validation of the soil moisture retrievals from the recently launched National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has generally limited past validation of remotely sensed soil moisture products to densely instrumented sites covering an area approximating the satellite ground footprint. However, by leveraging independent soil moisture information obtained from land surface modeling and/or alternative remote sensing products, triple collocation (TC) techniques offer a strategy for characterizing upscaling errors in sparser ground measurements and removing the impact of such error on the evaluation of remotely sensed soil moisture products. Here, we propose and validate a TC-based strategy designed to utilize existing sparse soil moisture networks (typically with a single sampling point per satellite footprint) to obtain an unbiased correlation validation metric for satellite surface soil moisture retrieval products. Application of this TC strategy at five SMAP core validation sites suggests that unbiased estimates of correlation between the satellite product and the true footprint average can be obtained - even in cases where ground observations provide only one single reference point within the footprint. An example of preliminary validation results from the application of this TC strategy to the SMAP Level 2 Soil Moisture Passive (beta release version) product is presented.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • The Role of Climatic Anomalies and Soil Moisture in the Decline of
           Drought-Prone Forests
    • Authors: David Chaparro;Jordi Vayreda;Mercè Vall-llossera;Mireia Banqué;Maria Piles;Adriano Camps;Jordi Martínez-Vilalta;
      Pages: 503 - 514
      Abstract: Increased intensity and duration of droughts and high-temperature events have been associated with forest decline in many parts of the world, and these decline events are expected to become more common under climate change. There is, therefore, a need for monitoring and modeling of forest decline. We used a regional forest condition monitoring program (DEBOSCAT) to study the spatial distribution of decline events in 2012 in Catalonia (Northeastern Spain) and their relationship with climatic factors. In 2012, this dataset was collected after an extraordinarily dry summer, and allowed the study of decline events in eight dominant tree species. We fitted a logistic model to predict forest decline probability as a function of species, precipitation and temperature anomalies, solar radiation, and remotely sensed soil moisture data from the Soil Moisture and Ocean Salinity Mission (SMOS). Broadleaved species were more affected by decline events than conifers. The statistical model explained almost 40% of forest decline occurrence, wherein almost 50% of this variability was explained by species effect, with broadleaved trees being generally more sensitive to the studied factors than conifers. Climatically wetter areas and those more exposed to radiation were more likely to be affected, suggesting better adaptation of forests in dry areas. In general, more damaged forests were characterized by high-positive temperature anomalies, lower than average rainfall, and low soil moisture in summer 2012. The most vulnerable species was Fagus sylvatica, a Euro-Siberian species, contrasting with Pinus halepensis, a typically Mediterranean species, which showed low sensitivity to drought.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Quantifying Grazing Intensity in China Using High Temporal Resolution
           MODIS Data
    • Authors: Fei Li;Yongfei Bai;Hongwei Wan;Jiajia Zheng;Juhua Luo;Dan Zhao;Po Liu;
      Pages: 515 - 523
      Abstract: Grassland as a distinct landscape type sustains biological and cultural diversity. However, its use at an unprecedentedly high intensity has had a disastrous impact on grassland ecosystems, with degeneration occurring nationwide. Traditional management policy, drafted according to the stocking rate from livestock statistics and/or in situ assessment, seems to have played no role in alleviating this trend. A major cause is the failure of decision making to consider spatial heterogeneity in the impact of grazing on grassland. To address this issue, high temporal resolution moderate-resolution imaging spectroradiometer data were used to develop a new indicator, named the grazing intensity index (GII), for quantifying grazing intensity (GI). The new index was compared with two traditional approaches and the results demonstrated that GII could substitute for both of them to quantify GI.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Mapping Human Settlements and Population at Country Scale From VHR Images
    • Authors: Lionel Gueguen;Jan Koenig;Carl Reeder;Tim Barksdale;Jon Saints;Kostas Stamatiou;Jeffery Collins;Carolyn Johnston;
      Pages: 524 - 538
      Abstract: Accurate and topical spatial datasets representing human populations are foundational to solving humanitarian issues. This paper provides unique solutions to accurately map human settlements both at scale and across remote areas. A method of village boundary extraction from very high resolution optical satellite imagery is proposed. Furthermore, the supplement of a crowd-sourced validation process to filter the detections for higher accuracy and the automated mosaic techniques are detailed. To demonstrate the computational and informational scalability of the process, four distinct geographic locations in Nigeria, Somalia, Pakistan, and Afghanistan were analyzed for a total processed area of 592 000 km2, comprised of 1159 high-resolution DigitalGlobe images. The geographic variability of the locations and the scale of the projects required dynamic and automated solutions, respectively. The curated results exhibit high recall and precision of human settlement data in remote as well as urban areas. Crowdsourced validation allows complete control over the precision of the final village boundary layer, and given time, an effective 100% precision can be achieved. This highly scalable and precise system is perfectly adequate for processing regional and country-scale areas, with minimal human effort.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Monitoring the Morphological Transformation of Beijing Old City Using
           Remote Sensing Texture Analysis
    • Authors: Antoine Lefebvre;Thomas Corpetti;
      Pages: 539 - 548
      Abstract: This paper is concerned with the morphological analysis of Beijing old city's dynamics from 1966 to 2015. This area has been continuously submitted to internal transformations since the opening of China to a market economy. In particular, districts of small traditional houses are being replaced by large buildings, entailing a fast reorganization of the inner city. To monitor this phenomenon, we propose to characterize urban patterns with very-high-resolution images using texture analysis. To this end, dedicated urban descriptors at various scales (based on local variance, cooccurrence matrices, and wavelets) are evaluated and selected to highlight informations related to different urban patterns. These features, whose scales are essential for a reliable description, are used to highlight changes in the city of Beijing in 21 images from 1969 to 2015. The experimental results show good performances and are in accordance with expert knowledge issued from Beijing urban planning studies. About 50% of the old urban pattern has been destroyed and most of these changes occurred before 2001.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Global Isostatic Gravity Maps From Satellite Missions and Their
           Applications in the Lithospheric Structure Studies
    • Authors: Robert Tenzer;Mohammad Bagherbandi;Wenjin Chen;Lars E. Sjöberg;
      Pages: 549 - 561
      Abstract: Recent satellite gravity missions provide information on the Earth's gravity field with a global and homogenous coverage. These data have been utilized in geoscience studies to investigate the Earth's inner structure. In this study, we use the global gravitational models to compute and compare various isostatic gravity data. In particular, we compile global maps of the isostatic gravity disturbances by applying the Airy-Heiskanen and Pratt-Hayford isostatic theories based on assuming a local compensation mechanism. We further apply the Vening Meinesz-Moritz isostatic (flexural) model based on a more realistic assumption of the regional compensation mechanism described for the Earth's homogenous and variable crustal structure. The resulting isostatic gravity fields are used to analyze their spatial and spectral characteristics with respect to the global crustal geometry. Results reveal that each of the applied compensation model yields a distinctive spatial pattern of the isostatic gravity field with its own spectral characteristics. The Airy-Heiskanen isostatic gravity disturbances provide a very smooth gravity field with no correlation with the crustal geometry. The Pratt-Hayford isostatic gravity disturbances are spatially highly correlated with the topography on land, while the Vening-Meinesz Moritz isostatic gravity disturbances are correlated with the Moho geometry. The complete crust-stripped isostatic gravity disturbances reveal a gravitational signature of the mantle lithosphere. These general characteristics provide valuable information for selection of a particular isostatic scheme, which could be used for gravimetric interpretations, depending on a purpose of the study.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Forward-Looking Radar Imaging: A Comparison of Two Data Processing
           Strategies
    • Authors: Francesco Soldovieri;Gianluca Gennarelli;Ilaria Catapano;D. Liao;Traian Dogaru;
      Pages: 562 - 571
      Abstract: This paper aims at comparing the conventional backpropagation algorithm and a microwave tomographic approach in the framework of forward-looking ground-penetrating radar imaging. The reconstruction capabilities of both methods are investigated in terms of achievable resolution limits for a sensing configuration similar to the Synchronous Impulse Reconstruction radar developed at the U.S. Army Research Laboratory. Furthermore, reconstruction results obtained by processing full-wave simulated data are shown with the goal of comparing the capability of both methods to detect buried and surface targets in scenarios emulating realistic operation conditions.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Azimuth High Resolution for a Conically Scanned Pencil-Beam Scatterometer
           Using Rotating Azimuth Doppler Discrimination
    • Authors: Gang Wang;Xiaolong Dong;Di Zhu;Qingliu Bao;
      Pages: 572 - 579
      Abstract: In order to satisfy a relatively high resolution for the retrieval of snow water equivalent, an X/Ku-band dual-frequency full-polarized SCATterometer (DFPSCAT) onboard Water Cycle Observation Mission (WCOM) satellite is designed for high-resolution observations. However, given the following situations, the method called “rotating azimuth Doppler discrimination” is proposed, which can satisfy the resolution requirement and real-time processing: 1) the conically rotation rate of antenna is relatively fast; 2) the swath width is larger than 1000 km; and 3) day or night observation capabilities are required. Considering the complexity of the system's design and the improvement of azimuth resolution capability, a burst pulsing scheme is addressed to satisfy the numbers of azimuth sampling. The simulation model is used to analyze the feasibility of azimuth discrimination method based on geometry and system parameters. It is shown that the achievable azimuth resolution is about 2-5 km at far end of the swath and only 5km at near end of the swath. The results show that when the size of a slice is set as 2-5 km, the Kpc is about less than 0.4 as snow depth varies, and the Kpc of combined slices is smaller than a single slice.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Unsupervised Classification of Polarimetirc SAR Image Via Improved
           Manifold Regularized Low-Rank Representation With Multiple Features
    • Authors: Bo Ren;Biao Hou;Jin Zhao;Licheng Jiao;
      Pages: 580 - 595
      Abstract: In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image unsupervised classification method is proposed. It combines three typical features, including polarimetric data features (coherent matrix), polarimetric decomposition features (Krogager, Freeman, Yamaguqi, Neuman, and H/A/α decomposition), and gray-level co-occurrence matrix features to comprehensively describe the data characteristics. And it also proposes a symmetric revised Wishart (SRW) distance-derived manifold regularized low-rank representation (SRWM_LRR) method to deeply exploit the geometry data structure. The low-rank representation (LRR) is used to capture the intrinsic global structure of PolSAR data and the manifold regularization is employed to detect the local structure of the data, in which SRW distance is introduced to measure the similarity between different pixels for describing the local manifold structure. This algorithm considers the specific statistics property in PolSAR data and simultaneously integrates multiple features in perspective of data geometry structure to represent pixels for achieving a better classification performance. The effectiveness and practicability of the proposed method are demonstrated by datasets obtained either in spaceborne or airborne SAR system, including the Flevoland dataset (AIRSAR L-Band) extensively used in land classification cover, and Xi'an dataset (RadarSAT-2 C-Band). Compared with the traditional Wishart classifier, Euclidean and SRW distance-based spectral clustering and LRR, the proposed method shows improvement in accuracy and efficiency as well as a better visualization result.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Nationwide Railway Monitoring Using Satellite SAR Interferometry
    • Authors: Ling Chang;Rolf P. B. J. Dollevoet;Ramon F. Hanssen;
      Pages: 596 - 604
      Abstract: Satellite synthetic aperture radar interferometry (InSAR) has the capability to monitor railway tracks and embankments with millimeter-level precision over wide areas. The potential of detecting differential deformation along the tracks makes it one of the most powerful and economical means for monitoring the safety and stability of the infrastructure on a weekly basis. Yet, the mere capability to detect such small deformations is not sufficient for an operational application of the technique. Handling huge data volumes, homogenizing independent datasets, and the connection with expert knowledge to identify risk areas are challenges to overcome. Here, we use a probabilistic method for InSAR time series postprocessing to efficiently scrutinize the data and detect railway instability. Moreover, to detect high-strain segments of the railway, we propose a short-arc-based method to focus on localized differential deformation between nearby InSAR measurement points. Our approach is demonstrated over the entire railway network of the Netherlands, more than 3000 km long, using hundreds of Radarsat-2 acquisitions between 2010 and 2015, leading to the first satellite-based nationwide railway monitoring system.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Efficient ISAR Autofocus Technique Using Eigenimages
    • Authors: Seong-Hyeon Lee;Ji-Hoon Bae;Min-Seok Kang;Kyung-Tae Kim;
      Pages: 605 - 616
      Abstract: In this paper, we propose a new and efficient inverse synthetic aperture radar (ISAR) autofocus technique by introducing eigenimages to boost the speed of the traditional autofocus algorithms. First, a preprocessing step is applied to mitigate the noise components in the received data. Then, we perform an eigen-decomposition of the covariance matrix of the range-aligned data, and generate the signal eigenimage obtained by deriving the Fourier transform of a small number of eigenvectors corresponding to the dominant eigenvalues. Finally, traditional autofocus methods are combined with the proposed signal eigenimage rather than the original ISAR image to eliminate image blurring due to phase errors. The proposed method can significantly lower the computational complexity of the traditional autofocus methods because the dimensionality of the signal eigenimage is considerably smaller than that of the ISAR image. Despite the low dimensionality of the signal eigenimages, the proposed scheme provides well-focused ISAR images that are comparable to those of the traditional autofocus methods in terms of image focal quality. Several simulations and experimental results using measured data of an actual flying aircraft are presented to verify the effectiveness of the proposed method.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Modified Multichannel Reconstruction Method of SAR With Highly Nonuniform
           Spatial Sampling
    • Authors: Na Liu;Robert Wang;Yunkai Deng;Shuo Zhao;Xiangyu Wang;
      Pages: 617 - 627
      Abstract: The traditional filter bank algorithm (FBA) for a multichannel synthetic aperture radar (SAR) system can provide an unambiguous recovery. However, the FBA may result in a seriously signal-to-noise ratio (SNR) loss or might even fail to reconstruct the spectrum if there is highly nonuniform sampling in azimuth. It is the general case because pulse repetition frequency (PRF) cannot be selected flexibly for spaceborne imaging system. In this paper, a new algorithm for processing highly nonuniform sampled SAR data to improve the SNR is presented. The proposed method is based on the operation of ambiguity-index screening (AIS) and the minimum mean square error (MMSE) criterion. It can be utilized to reconstruct the spectrum even in the presence of highly nonuniform sampling or coinciding sampling. The approach can significantly improve the SNR especially for the highly nonuniform sampling, and the azimuth ambiguity-to-signal ratio obtained by the proposed method is superior to the traditional one for a wide range of PRF. The proposed algorithm is validated by the simulated data and the real two-channel radar raw data acquired by C-band airborne SAR system with a bandwidth of 210 MHz, which is designed by the Department of Space Microwave Remote Sensing System, Institute of Electronics, Chinese Academy of Sciences.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Region-Line Association Constraints for High-Resolution Image Segmentation
    • Authors: Min Wang;Jiru Huang;Dongping Ming;
      Pages: 628 - 637
      Abstract: Man-made objects, including buildings and roads, often feature straight boundaries. Specific shape features, including parallel boundaries (e.g., roads and buildings) and perpendicular corners (e.g., buildings), are strong structural clues for distinguishing man-made objects from natural objects. In this study, such observations are implemented in remote-sensing image segmentation. Several region-line association constraints are proposed, including the parallel straight line (PLSL) neighborhood, the perpendicular straight line (PPSL) neighborhood, and the PLSL zone. A region-merging process with these structural constraints is appended to the hard-boundary-constrained segmentation method (HBC-SEG), which is a multiscale image segmentation method. Results show that, compared with HBC-SEG, the proposed method presents a significant decrease in oversegmentation errors (measured by recall ratio r) and an insignificant increase in undersegmentation errors (measured by precision p). As a result, measure m2, which synthetically evaluates undersegmentation and oversegmentation errors, increases significantly. The improved method helps obtain complete objects, thus facilitating feature extraction and image classification for object-based image analysis.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Simulation of Multiangular Remote Sensing Products Using Small Satellite
           Formations
    • Authors: Sreeja Nag;Charles K. Gatebe;Thomas Hilker;
      Pages: 638 - 653
      Abstract: To completely capture the multiangular reflectance of an opaque surface, one must estimate the bidirectional reflectance distribution function (BRDF), which seeks to represent variations in surface reflectance as a function of measurement and illumination angles at any time instant. The gap in angular sampling abilities of existing single satellites in Earth observation missions can be complemented by small satellites in formation flight. The formation would have intercalibrated spectrometer payloads making reflectance measurements, at many zenith and azimuthal angles simultaneously. We use a systems engineering tool coupled with a science evaluation tool to demonstrate the performance impact and mission feasibility. Formation designs are generated and compared to each other and multisensor single spacecraft, in terms of estimation error of BRDF and its dependent products such as albedo, light use efficiency (LUE), and normalized difference vegetation index (NDVI). Performance is benchmarked with respect to data from previous airborne campaigns (NASA's Cloud Absorption Radiometer), and tower measurements (AMSPEC II), and assuming known BRDF models. Simulations show that a formation of six small satellites produces lesser average error (21.82%) than larger single spacecraft (23.2%), purely in terms of angular sampling benefits. The average monolithic albedo error of 3.6% is outperformed by a formation of three satellites (1.86%), when arranged optimally and by a formation of seven to eight satellites when arranged in any way. An eight-satellite formation reduces albedo errors to 0.67% and LUE errors from 89.77% (monolithic) to 78.69%. The average NDVI for an eight satellite, nominally maintained formation is better than the monolithic 0.038.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • A New Building Extraction Postprocessing Framework for
           High-Spatial-Resolution Remote-Sensing Imagery
    • Authors: Xin Huang;Wenliang Yuan;Jiayi Li;Liangpei Zhang;
      Pages: 654 - 668
      Abstract: In conjunction with the recently developed morphological building index (MBI), the proposed postprocessing framework describes the characteristics of buildings by simultaneously considering the spectral, geometrical, and contextual information, and can be successfully applied to large high-spatial-resolution images. In this way, the proposed framework can alleviate the amount of false alarms to a remarkable extent, which mainly come from the bright soil and vegetation in rural and mountainous areas. Validated on a series of large test images obtained by the widely used commercial satellite sensors, the experiments confirm the promising performance of the proposed framework over various areas, including urban, mountainous, rural, and agricultural areas. Furthermore, the proposed framework increases the quality index by 11% and 9% on average compared to the performance of the original MBI and DMP-SVM, respectively. In addition, the parameter sensitivity is analyzed in detail and appropriate ranges of the parameters are suggested. The proposed building detection framework is designed to be of practical use for building detection from high-resolution imagery.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Mapping and Monitoring Urban Ecosystem Services Using Multitemporal
           High-Resolution Satellite Data
    • Authors: Jan Haas;Yifang Ban;
      Pages: 669 - 680
      Abstract: This study aims at providing a new method to efficiently analyze detailed urban ecological conditions at the example of Shanghai, one of the world's most densely populated megacities. The main objective is to develop a method to effectively analyze high-resolution optical satellite data for mapping of ecologically important urban space and to evaluate ecological changes through the emerging ecosystem service supply and demand concept. Two IKONOS and GeoEye-1 scenes were used to determine land use/land cover change in Shanghai's urban core from 2000 to 2009. After preprocessing, the images were segmented and classified into seven distinct urban land use/land cover classes through SVM. The classes were then transformed into ecosystem service supply and demand budgets for regulating, provisioning and cultural services, and ecological integrity based on ecosystem functions. Decreases in continuous urban fabric and industrial areas in the favor of urban green sites and high-rise areas with commercial/residential function could be observed resulting in an increase of at least 20% in service supply budgets. Main contributor to the change is the decrease in continuous urban fabric and industrial areas. The overall results and outcome of the study strengthen the suggested application of the proposed method for urban ecosystem service budget mapping with hitherto for that purpose unutilized high-resolution data. The insights and results from this study might further contribute to sustainable urban planning, prove common grounds for interurban comparisons, or aid in enhancing ecological intraurban functionality by analyzing the distribution of urban ecospace and lead to improved accessibility and proximity to ecosystem services in urban areas.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • A Set of Methods to Support Object-Based Distributed Analysis of Large
           Volumes of Earth Observation Data
    • Authors: Rodrigo S. Ferreira;Cristiana Bentes;Gilson A. O. P. Costa;Dário A. B. Oliveira;Patrick N. Happ;Raul Q. Feitosa;Paolo Gamba;
      Pages: 681 - 690
      Abstract: The rapid increase in the number of aerial and orbital Earth observation systems is generating a huge amount of remote sensing data that need to be readily transformed into useful information for policy and decision makers. This exposes an urgent demand for image interpretation tools that can deal efficiently with very large volumes of data. In this work, we introduce a set of methods that support distributed processing of georeferenced raster and vector data in a computer cluster, which may be a virtual cluster provided by cloud computing infrastructure services. The set of methods comprise a particular technique for indexing distributed georeferenced datasets, as well as strategies for distributing efficiently the processing of spatial context-aware operations. They provide the means for the development of scalable applications, capable of processing large volumes of geospatial data. We evaluated the proposed methods in a remote sensing image interpretation application, built on the MapReduce framework, and executed in a cloud computing infrastructure. The experimental results corroborate the capacity of the methods to support efficient handling of very large earth observation datasets.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Class-Oriented Spectral Partitioning for Remotely Sensed Hyperspectral
           Image Classification
    • Authors: Yi Liu;Jun Li;Peijun Du;Antonio Plaza;Xiuping Jia;Xinchang Zhang;
      Pages: 691 - 711
      Abstract: Remotely sensed hyperspectral images exhibit very high dimensionality in the spectral domain. As opposed to band selection techniques, which extract a subset of the original spectral bands in the image, spectral partitioning (SP) techniques reassign the original bands into subgroups that are then processed separately. From a classification perspective, this strategy has the advantage that all the original information in the hyperspectral data can be retained while addressing the curse of dimensionality given by the Hughes phenomenon. Even if SP prior to classification has been widely used, the strategies adopted to perform such partitioning did not consider the diversity of spectral classes in the scene. In other words, available techniques are not driven by the information contained in the classes of interest, which can be very useful to perform the SP in a more effective manner for classification purposes. To address this issue, in this paper, we present a new class-oriented SP technique that exploits prior information about the classes by automatically ranking the spectral bands that are more useful for each specific class (instead of considering the hyperspectral image as a whole). The resulting multiple subgroups of bands with lower dimensionality are then fed to a multiple classifier system. Our experimental results, conducted with three different hyperspectral airborne images, suggest that the presented method leads to competitive results when compared to other state-of-the-art approaches in the field.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Active Deep Learning for Classification of Hyperspectral Images
    • Authors: Peng Liu;Hui Zhang;Kie B. Eom;
      Pages: 712 - 724
      Abstract: Active deep learning classification of hyperspectral images is considered in this paper. Deep learning has achieved success in many applications, but good-quality labeled samples are needed to construct a deep learning network. It is expensive getting good labeled samples in hyperspectral images for remote sensing applications. An active learning algorithm based on a weighted incremental dictionary learning is proposed for such applications. The proposed algorithm selects training samples that maximize two selection criteria, namely representative and uncertainty. This algorithm trains a deep network efficiently by actively selecting training samples at each iteration. The proposed algorithm is applied for the classification of hyperspectral images, and compared with other classification algorithms employing active learning. It is shown that the proposed algorithm is efficient and effective in classifying hyperspectral images.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • GSEAD: Graphical Scoring Estimation for Hyperspectral Anomaly Detection
    • Authors: Rui Zhao;Liangpei Zhang;
      Pages: 725 - 739
      Abstract: Hyperspectral anomaly detection has been the subject of increased attention in the past 20 years. One obvious trend for scholars is seeking an appropriate data description in the hyperspectral anomaly detection domain. However, a specific predetermined data model in a given detector may not be able to fit all the other cases of hyperspectral images. Hence, can we construct a hyperspectral anomaly detector from a data-adaptive analysis perspective that can implement detection processing only with the characteristics of the data itself? In our manuscript, we propose a graphical scoring estimation based anomaly detector (GSEAD) that utilizes graphical data description to achieve a data-adaptive analysis-based anomaly detection procedure. First, potential anomalies are screened out by a predicted connected component graph (pcc-graph). The remaining pixels constitute the robust background dataset. Second, an embedded locality preserving graph (elp-graph) is generated with the robust background dataset in an intrinsic manifold space by locality preserving graph embedding. Finally, a k-nearest neighbor graphical scoring estimation is undertaken to output the detection result. A target-embedded hyperspectral dataset and three real hyperspectral images were utilized to validate the detection performance of the proposed method. The experimental results show that GSEAD achieves superior receiver operating characteristic curves, area under ROC curves values, and background-anomaly separation than some of the other state-of-the-art anomaly detection methods. A sensitivity analysis of the relevant parameters was also undertaken in the experimental analysis.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Mapping Mosaic Virus in Sugarcane Based on Hyperspectral Images
    • Authors: Érika Akemi Saito Moriya;Nilton Nobuhiro Imai;Antonio Maria Garcia Tommaselli;Gabriela Takahashi Miyoshi;
      Pages: 740 - 748
      Abstract: The aim of this research was to develop a methodology involving aerial surveying using an unmanned aerial system (UAS), processing and analysis of images obtained by a hyperspectral camera, achieving results that enable discrimination and recognition of sugarcane plants infected with mosaic virus. It was necessary to characterize the spectral response of healthy and infected sugarcane plants in order to define the correct mode of operation for the hyperspectral camera, which provides many spectral band options for imaging but limits each image to 25 spectral bands. Spectral measurements of the leaves of infected and healthy sugarcane with a spectroradiometer were used to produce a spectral library. Once the most appropriate spectral bands had been selected, it was possible to configure the camera and carry out aerial surveying. The empirical line approach was adopted to obtain hemispherical conical reflectance factor values with a radiometric block adjustment to produce a mosaic suitable for the analysis. A classification based on spectral information divergence was applied and the results were evaluated by Kappa statistics. Areas of sugarcane infected with mosaic were identified from these hyperspectral images acquired by UAS and the results obtained had a high degree of accuracy.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Coregistration of Satellite Images and Airborne LiDAR Data Through the
           Automatic Bias Reduction of RPCs
    • Authors: Alireza Safdarinezhad;Mehdi Mokhtarzade;Mohammad Javad Valadan Zoej;
      Pages: 749 - 762
      Abstract: Rational polynomial coefficients (RPCs), which are provided for most of the commercial satellite images, match relevant image locations to their three-dimensional-ground positions. In spite of their key role in most of remote sensing analysis, a considerable amount of bias is usually seen in the RPCs that necessitate their refinement as a common preprocessing. This refinement is typically performed using some complimentary control information (e.g., ground control points). This paper proposes a matching scheme between the georeferenced airborne LiDAR data and high-resolution satellite images (HRSI) to automatically obtain the control data that is required for RPCs bias compensation. The main contribution of this paper is the design and implementation of a shadow-based matching strategy to correspond the inherently different HRSI and LiDAR data. In this process, RPCs are regarded as the initial relating model between HRSI and LiDAR data and the existing bias of these parameters are ultimately reduced. A novel method, called incremental clustering, is used to automatically detect the shadow cast from the HRSI. In addition, a geometrical method is developed for shadow reconstructions from LiDAR data. These generated shadow maps are then assimilated from geometrical point of view as well as coordinate systems. Finally, a frequency domain matching is performed to find and compensate for the existent bias in RPCs. The obtained results indicate a precise bias reduction of the RPCs, where the initial misalignment of georeferencing is improved from 18 m (30 pixels) to about 0.58 m (1 pixel).
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Snake Energy Analysis and Result Validation for a Mobile Laser Scanning
           Data-Based Automated Road Edge Extraction Algorithm
    • Authors: Pankaj Kumar;Paul Lewis;Conor P. McElhinney;Pawel Boguslawski;Tim McCarthy;
      Pages: 763 - 773
      Abstract: The negative impact of road accidents cannot be ignored in terms of the very sizeable social and economic loss. Road infrastructure has been identified as one of the main causes of the road accidents. They are required to be recorded, located, measured, and classified in order to schedule maintenance and identify the possible risk elements of the road. Toward this, an accurate knowledge of the road edges increases the reliability and precision of extracting other road features. We have developed an automated algorithm for extracting road edges from mobile laser scanning (MLS) data based on the parametric active contour or snake model. The algorithm involves several internal and external energy parameters that need to be analyzed in order to find their optimal values. In this paper, we present a detailed analysis of the snake energy parameters involved in our road edge extraction algorithm. Their optimal values enable us to automate the process of extracting edges from MLS data for tested road sections. We present a modified external energy in our algorithm and demonstrate its utility for extracting road edges from low and nonuniform point density datasets. A novel validation approach is presented, which provides a qualitative assessment of the extracted road edges based on direct comparisons with reference road edges. This approach provides an alternative to traditional road edge validation methodologies that are based on creating buffer zones around reference road edges and then computing quality measure values for the extracted edges. We tested our road edge extraction algorithm on datasets that were acquired using multiple MLS systems along various complex road sections. The successful extraction of road edges from these datasets validates the robustness of our algorithm for use in complex route corridor environments.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Segmentation of Individual Trees From TLS and MLS Data
    • Authors: Lishan Zhong;Liang Cheng;Hao Xu;Yang Wu;Yanming Chen;Manchun Li;
      Pages: 774 - 787
      Abstract: Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) data can be used to obtain abundant and precise side information of trees. Therefore, it can enable extracting individual tree parameters, such as the tree height, crown size, crown base height, and diameter at breast height, and it can provide basic data for forest research and management. This study proposes a technical framework for segmenting individual trees from TLS and MLS data. This framework contains six steps: 1) data preprocessing, 2) octree construction, 3) spatial clustering, 4) stem detection, 5) initial segmentation, and 6) overlapped canopy segmentation. This framework makes two main contributions: 1) a top-down hierarchical segmentation approach, including connectivity-based spatial clustering (regional scale), stem-based initial segmentation (individual tree scale), and fine segmentation of overlapped canopy (canopy scale), is proposed to reduce technical difficulties and improve process efficiency; and 2) a modified node similarity calculation for normalized cut method aiming at segmenting overlapped canopy, which can effectively separate neighboring trees even if their canopies are overlapped, is proposed. The proposed framework was tested on a leaves-off terrestrial LiDAR dataset and a leaves-on mobile LiDAR dataset. For terrestrial LiDAR data, our framework achieved completeness of 92.4%, correctness of 95.4%, and F-score of 0.94. For mobile LiDAR data, the corresponding values were 94.0%, 93.7%, and 0.94.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
  • Proceedings of the IEEE
    • Pages: 788 - 788
      Abstract: Advertisement, IEEE.
      PubDate: Feb. 2017
      Issue No: Vol. 10, No. 2 (2017)
       
 
 
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