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  Subjects -> ELECTRONICS (Total: 154 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 6)
Advances in Electrical and Electronic Engineering     Open Access   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
Advances in Microelectronic Engineering     Open Access   (Followers: 11)
Advances in Power Electronics     Open Access   (Followers: 20)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 197)
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: 31)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 40)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 13)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 88)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 72)
Electronic Markets     Hybrid Journal   (Followers: 8)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 56)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 60)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 40)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 3)
Energy Storage Materials     Full-text available via subscription   (Followers: 1)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 13)
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: 128)
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: 42)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 29)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 8)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal   (Followers: 1)
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 46)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 43)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 46)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 15)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 31)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 13)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 22)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 51)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 7)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 13)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 7)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 14)
IET Power Electronics     Hybrid Journal   (Followers: 26)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 17)
IETE Journal of Education     Open Access   (Followers: 4)
IETE Journal of Research     Open Access   (Followers: 9)
IETE Technical Review     Open Access   (Followers: 11)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 7)
Informatik-Spektrum     Hybrid Journal   (Followers: 1)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 8)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 16)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 7)
International Journal of Antennas and Propagation     Open Access   (Followers: 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: 3)
International Journal of Electronics & Data Communication     Open Access   (Followers: 10)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 12)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal   (Followers: 2)
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 9)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 6)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 3)
International Journal of Power Electronics     Hybrid Journal   (Followers: 13)
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: 13)
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: 9)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription   (Followers: 1)
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 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: 5)
Journal of Electronics (China)     Hybrid Journal   (Followers: 4)
Journal of Energy Storage     Full-text available via subscription  
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 123)
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: 4)
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: 8)
Journal of Semiconductors     Full-text available via subscription   (Followers: 3)
Journal of Sensors     Open Access   (Followers: 20)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 14)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 7)
Metrology and Measurement Systems     Open Access   (Followers: 4)
Microelectronics and Solid State Electronics     Open Access   (Followers: 14)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 33)
Nanotechnology, Science and Applications     Open Access   (Followers: 4)
Networks: an International Journal     Hybrid Journal   (Followers: 5)
Open Journal of Antennas and Propagation     Open Access   (Followers: 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: 8)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 4)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 48)
Semiconductors and Semimetals     Full-text available via subscription   (Followers: 1)
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 2)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 57)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 10)
Solid-State Electronics     Hybrid Journal   (Followers: 7)
Superconductor Science and Technology     Hybrid Journal   (Followers: 2)
Synthesis Lectures on Power Electronics     Full-text available via subscription   (Followers: 2)
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 6)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 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: 12)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.196]   [H-I: 37]   [48 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • IEEE Geoscience and Remote Sensing Societys
    • PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Information for Authors
    • PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Institutional Listings
    • PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Parallel Construction of the WRF Pleim-Xiu Land Surface Scheme With Intel
           Many Integrated Core (MIC) Architecture
    • Authors: Melin Huang;Bormin Huang;Hung-Lung Allen Huang;
      Pages: 1239 - 1246
      Abstract: The weather research and forecast model (WRF), a simulation model, is built for the needs of both research and operational weather forecast and research in atmospheric science. The land-surface model (LSM), which describes one physical process in atmosphere, supplies the heat and moisture fluxes over land points and sea-ice points. Among several schemes of LSM that have been developed and incorporated into WRF, Pleim-Xiu (PX) scheme is one popular scheme in LSM. Processing the WRF simulation codes for weather prediction has acquired the benefits of dramatically increasing computing power with the advent of large-scale parallelism. Several merits such as vectorization essence, efficient parallelization, and multiprocessor computer structure of Intel Many Integrated Core (MIC) architecture allow us to accelerate the computation performance of the modeling code of the PX scheme. This paper demonstrates that our results of the optimized MIC-based PX scheme executing on Xeon Phi coprocessor 7120P enhances the computing performance by 2.3× and 11.7×, respectively, in comparison to the initial CPU-based code running on one CPU socket (eight cores) and on one single CPU core with Intel Xeon E5-2670.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Parallel Implementation of Spatial–Spectral Endmember Extraction on
           Graphic Processing Units
    • Authors: Luis Ignacio Jiménez;Sergio Sánchez;Gabriel Martín;Javier Plaza;Antonio J. Plaza;
      Pages: 1247 - 1255
      Abstract: The identification of pure spectral signatures (endmembers) in remotely sensed hyperspectral images has traditionally focused on the spectral information alone. Recently, techniques such as the spatial–spectral endmember extraction (SSEE) have incorporated both the spectral and the spatial information contained in the scene. Since hyperspectral images contain very detailed information in the spatial and spectral domain, the integration of these two sources of information generally comes with a significant increase in computational complexity. In this paper, we develop a new computationally efficient implementation of SSEE using commodity graphics processing units (GPUs). The relevance of GPUs comes from their very low cost, compact size, and the possibility to obtain significant acceleration factors by exploiting properly the GPU hardware architecture. Our experimental results, focused on evaluating the candidate endmembers produced by SSEE and also the computational performance of the GPU implementation, indicated significant acceleration factors that allow exploiting the SSEE method in computationally efficient fashion.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Semantically Enhanced Catalogue Search Model for Remotely Sensed Imagery
    • Authors: Ya Lin;Hao Xu;Yuqi Bai;
      Pages: 1256 - 1264
      Abstract: Keyword-based search enabled by catalogue services is now the dominant way to query remotely sensed imagery. One of its major limitations is that searchable attributes have to be maintained in the underlying metadata database. This study investigates the feasibility of mediating semantic query and catalogue search together to allow more searchable parameters without any changes to the existing metadata database and catalogue service. Limitations of a catalogue's textual search capabilities are analyzed. A use case of searching for sea ice imagery using search criteria that are absent in the NASA ECHO (the U.S. National Aeronautics and Space Administration EOS Clearing House) catalogue service is presented. An ontology dedicated for remotely sensed sea ice data collections is introduced. Details of a two-step hybrid metadata search model, i.e., collection-level discovery search enabled by ontology query, and granule-level inventory search fulfilled by catalogue service, are presented and evaluated. Our results show that this semantically enhanced catalogue search model could easily extend the existing catalogue service to allow more searchable parameters, and, at the same time, maintain a backward compatibility with them. The lessons learned may be useful to others' modeling of characteristics associated with geoscience data collections, and thereby providing enhanced geoscience data search capabilities.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Gaussian Process Sensitivity Analysis for Oceanic Chlorophyll Estimation
    • Authors: Katalin Blix;Gustau Camps-Valls;Robert Jenssen;
      Pages: 1265 - 1277
      Abstract: Gaussian process regression (GPR) has experienced tremendous success in biophysical parameter retrieval in the past years. The GPR provides a full posterior predictive distribution so one can derive mean and variance predictive estimates, i.e., point-wise predictions and associated confidence intervals. GPR typically uses translation invariant covariances that make the prediction function very flexible and nonlinear. This, however, makes the relative relevance of the input features hardly accessible, unlike in linear prediction models. In this paper, we introduce the sensitivity analysis of the GPR predictive mean and variance functions to derive feature rankings and spectral spacings, respectively. The methodology can be used to uncover knowledge in any kernel-based regression method, it is fast to compute, and it is expressed in closed-form. The methodology is evaluated on GPR for global ocean chlorophyll prediction, revealing the most important spectral bands and their spectral spacings. We illustrate the (successful) methodology in several datasets and sensors.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Denoising Smooth Signals Using a Bayesian Approach: Application to
    • Authors: Abderrahim Halimi;Gerald S. Buller;Stephen McLaughlin;Paul Honeine;
      Pages: 1278 - 1289
      Abstract: This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous signals that can have a numerical or an analytical expression with respect to some parameters. The proposed Bayesian model takes into account the Gaussian properties of the noise and the smooth evolution of the successive signals. In addition, a gamma Markov random field prior is assigned to the signal energies and to the noise variances to account for their known properties. The resulting posterior distribution is maximized using a fast coordinate descent algorithm whose parameters are updated by analytical expressions. The proposed algorithm is tested on satellite altimetric data demonstrating good denoising results on both synthetic and real signals. In comparison with state-of-the-art algorithms, the proposed strategy provides a good compromise between denoising quality and necessary reduced computational cost. The proposed algorithm is also shown to improve the quality of the altimetric parameters when combined with a parameter estimation or a classification strategy.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Recent Glacier Dynamics in the Northern Novaya Zemlya Observed by Multiple
           Geodetic Techniques
    • Authors: Zhiyue Sun;Hyongki Lee;Yushin Ahn;Abureli Aierken;Kuo-Hsin Tseng;Modurodoluwa A. Okeowo;C. K. Shum;
      Pages: 1290 - 1302
      Abstract: In Novaya Zemlya (NVZ), melting marine-terminating glaciers have been identified to be the most significant contributor to the ice loss. However, the influence of influx on mass loss has not been discussed in previous studies. In this study, we present multiple geodetic observations of four glaciers along the Barents Sea coast to determine the influence of glacier outflow on net mass change by considering the mass gain from snowfall. We obtained the average ice loss rate of –1.04 ± 0.25 Gt year–1 during the period of 2003–2014 from the gravity recovery and climate experiment (GRACE) data. We discovered an interannual increase of 4.30 ± 0.97 Gt year–1 in 2007–2010 which had not been presented. In addition, we also observed two other interannual variations in the form of negative mass trends from GRACE during the periods of 2004–2007 and 2010–2014. A speckle-matching technique was applied on pairs of synthetic aperture radar data to fill the gap of velocity estimations for the period of 2007–2010 which were not reported in previous studies. The mass increase in 2007–2010 can be explained by the increase in influx while the change in outflow was negligible. In addition, we extended similar analysis to the periods of 2002–2007 and 2010–2014, and identified the contribution of outflow and influx to the mass change in NVZ. In particular, the mass losses in the periods of 2004–2007 and 2010–2013 in NVZ were related to the significant increase of outflow while positive influx anomalies were observed.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Assimilation of Leaf Area Index and Surface Soil Moisture With the
           CERES-Wheat Model for Winter Wheat Yield Estimation Using a Particle
           Filter Algorithm
    • Authors: Yi Xie;Pengxin Wang;Huitao Sun;Shuyu Zhang;Li Li;
      Pages: 1303 - 1316
      Abstract: To improve winter wheat yield estimates in the Guanzhong Plain, China, the daily leaf area index (LAI) and soil moisture at depths of 0–20 cm (θ) simulated by CERES-Wheat model were assimilated from field-measured LAI and θ and from Landsat-derived LAI and θ using a particle filter algorithm. Linear regression analyses were performed to determine the relationships between assimilated LAI or θ and field-measured yields to identify highly yield-related variables for each growth stage of winter wheat, which were used to establish an optimal-assimilation yield estimation model. At the green-up and milk stages, assimilated θ was highly correlated with the measured yields, and at the jointing and heading-filling stages, both assimilated LAI and θ were highly correlated with the yields. The optimal-assimilation yield estimation model was then established by combining the regression equations relating assimilated θ to the yields during the green-up and milk stages with the equations relating assimilated LAI and θ to the yields at the jointing and heading-filling stages, which resulted in better estimation accuracy than the yield estimation model established based on dualistic regression equations relating the assimilated LAI and θ to measured yields for each growth stage. Moreover, establishing different yield estimation models for irrigated and rain-fed farmlands improved the yield estimates compared with the established estimation model that did not take into account whether the farmlands were irrigated or rain-fed. Therefore, the assimilation of highly yield-related state variables at each wheat growth stage provides a reli-ble and promising method for improving crop yield estimates.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Assimilation of Synthetic Remotely Sensed Soil Moisture in Environment
           Canada's MESH Model
    • Authors: Xiaoyong Xu;Bryan A. Tolson;Jonathan Li;Bruce Davison;
      Pages: 1317 - 1327
      Abstract: With recent advances in satellite microwave soil moisture estimation, particularly the launch of the Soil Moisture and Ocean Salinity satellite and the soil moisture active passive mission, there is an increased demand for exploiting the potential of satellite microwave soil moisture observations to improve the predictive capability of hydrologic and land surface models. This study presents the implementation of the 1-D version of the ensemble Kalman filter scheme to assimilate satellite soil moisture into Environment Canada's Standalone Modélisation Environmentale-Surface et Hydrologie (MESH) model that couples the Canadian land surface scheme with a distributed hydrological model. This paper examines the performance of the established assimilation scheme by conducting a series of synthetic assimilation experiments in which the satellite soil moisture and the reference (“true”) solutions were derived from the MESH model simulations. The synthetic analyses have demonstrated the capability of the assimilation system, given the synthetic satellite soil moisture and the intentionally degraded model estimates, to accurately approximate the “true” surface layer and root-zone soil moisture solutions. The experiments have also revealed the impacts of a series of factors (ensemble size, vegetation cover, observing frequency, specification of observation, and model input error parameters) upon the quality of the assimilation estimates, which can provide an important guidance for the practical application of the assimilation scheme.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Intercomparison of Soil Moisture, Evaporative Stress, and Vegetation
           Indices for Estimating Corn and Soybean Yields Over the U.S.
    • Authors: Iliana E. Mladenova;John D. Bolten;Wade T. Crow;Martha C. Anderson;Christopher R. Hain;David M. Johnson;Rick Mueller;
      Pages: 1328 - 1343
      Abstract: This paper presents an intercomparative study of 12 operationally produced large-scale datasets describing soil moisture, evapotranspiration (ET), and/or vegetation characteristics within agricultural regions of the contiguous United States (CONUS). These datasets have been developed using a variety of techniques, including, hydrologic modeling, satellite-based retrievals, data assimilation, and survey/in-field data collection. The objectives are to assess the relative utility of each dataset for monitoring crop yield variability, to quantitatively assess their capacity for predicting end-of-season corn and soybean yields, and to examine the evolution of the yield-index correlations during the growing season. This analysis is unique both with regards to the number and variety of examined yield predictor datasets and the detailed assessment of the water availability timing on the end-of-season crop production during the growing season. Correlation results indicate that over CONUS, at state-level soil moisture and ET indices can provide better information for forecasting corn and soybean yields than vegetation-based indices such as normalized difference vegetation index. The strength of correlation with corn and soybean yields strongly depends on the interannual variability in yield measured at a given location. In this case study, some of the remotely derived datasets examined provide skill comparable to that of in-situ field survey-based data—further demonstrating the utility of these remote sensing-based approaches for estimating crop yield.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Analyzing the Impacts of Urbanization and Seasonal Variation on Land
           Surface Temperature Based on Subpixel Fractional Covers Using Landsat
    • Authors: Youshui Zhang;Heiko Balzter;Bin Liu;Yajun Chen;
      Pages: 1344 - 1356
      Abstract: Impervious surface areas (ISAs) and vegetation are two major urban land cover types. Estimating the spatial distribution of ISA and vegetation is critical for analyzing urban landscape patterns and their impact on the thermal environment. In this paper, linear spectral mixture analysis (LSMA) is used to extract their respective subpixel land cover composition from bitemporal Landsat images and the accuracy of the fractional covers is assessed with a subpixel confusion matrix at the category level and the map level by comparing with the reference data from high-resolution images. The percent ISA was divided into discrete categories representing different urban development density areas. Mean land surface temperature (LST) is calculated for each ISA category to analyze the thermal characteristics of different levels of development in the urban area of Fuzhou, China. ISA and vegetation variations are also quantified between different ISA categories and different dates. The contribution index is also calculated based on each ISA category to analyze the impact of different landscape patterns on the urban thermal environment. The results show that ISA category is an important determinant of the urban thermal environment. Furthermore, seasonal variations significantly impact the strength of this relationship. In the study area, the contribution indices were highest in the 90%–100% ISA category in summer 2013 and early spring 2001. The analytical methodologies used in this study can help to quantify urban thermal environmental functions under conditions of urban expansion and explore the climate adaptation potential of cities.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Spatiotemporal Analysis of MODIS Land Surface Temperature With In Situ
           Meteorological Observation and ERA-Interim Reanalysis: The Option of Model
    • Authors: Wei Liu;Shuisen Chen;Hao Jiang;Chongyang Wang;Dan Li;
      Pages: 1357 - 1371
      Abstract: The land surface temperature (Ts) is an important parameter in land surface and atmosphere studies. A set of synchronously observed “ground-truth” temperature as training data is required for some empirical/semiempirical statistical and neural network methods for retrieving Ts from passive microwave (PMW) remote sensing data. To provide information for the choice of the most suitable dataset in Ts retrieval of PMW remote sensing, the spatiotemporal comparison between the moderate-resolution imaging spectroradiometer Ts (MODIS Ts), the meteorologically observed Ts ( in situ Ts), the meteorologically observed near-surface air temperature (in situ Ta), and European Center for Medium-Range Weather Forecast reanalysis products, the ERA-Interim Ts (ERA Ts), in South China for each season's daytime and nighttime is conducted in this paper. Results show that a large discrepancy between the MODIS Ts and the in situ Ts exists, whereas the discrepancies between the MODIS Ts, the in situ Ta and the ERA Ts are relatively smaller in daytime. For nighttime period, the differences between each dataset are relatively much smaller. Because the MODIS Ts is representative at the satellite pixel scale, it has a smaller spatial-scale mismatch with PMW data compared to in situ meteorological observation. The MODIS Ts is suitable for both the daytime and the nighttime PMW Ts model calibration if it is synchronously observed under almost clear-sky condition. By contrast, for the PMW Ts model calibration within the daytime period, the synchronously obtained in situ Ts is not suitabl- to be used as training data. If the ground temperature of daytime period derived from PMW is required, but the MODIS Ts is unavailable, the in situ Ta should be selected as the “ground truth” for the model calibration. However, it should be noticed that the inversion results are the near-surface air temperature rather than the Ts. Remarkably, reanalysis products such as the ERA Ts presents an alternative choice for both the daytime and the nighttime Ts model calibration if there are no MODIS Ts products or in situ temperature available. After the comparison, an example of PMW Ts retrieval for nighttime period was given, showing a promising performance on deriving an applicable PMW Ts inversion model based on the selection of an appropriate training dataset.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Using Land Long-Term Data Records to Map Land Cover Changes in China Over
    • Authors: Haixing Li;Pengfeng Xiao;Xuezhi Feng;Yongke Yang;Lingxiao Wang;Wenbo Zhang;Xiaohui Wang;Weiding Feng;Xiao Chang;
      Pages: 1372 - 1389
      Abstract: China has undergone significant land cover changes since the 1980s. However, there are limited consistent and continuous dataset of national scale. Using advanced very high resolution radiometer and moderate resolution imaging spectrometer data from the land long-term data record, we developed a large-scale classification approach to produce a decadal 5-km resolution land cover dataset for China (ChinaLC) from 1981 to 2010. A total of 19 classes of training and validation samples were obtained from visual interpretation of high-resolution Google Earth images and historical vegetation maps. Combined efforts of standard criteria, rigid check, and detailed recording were conducted to strengthen the robustness of the multitemporal samples. The different compositions of metrics and parameters were tested to obtain the optimal support vector machine (SVM) classification results. The ChinaLC dataset has an average overall accuracy of approximately 75%, which is much higher compared with other large-scale land cover datasets. Furthermore, a high consistency was found between the land cover changes of ChinaLC and other studies using higher spatial resolution data. The decadal spatial–temporal transition patterns were analyzed and the important reasons for accelerated landscape changes were also explained over the 30 years.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Delineating Individual Tree Crowns in an Uneven-Aged, Mixed Broadleaf
           Forest Using Multispectral Watershed Segmentation and Multiscale Fitting
    • Authors: Jian Yang;Yuhong He;John P. Caspersen;Trevor A. Jones;
      Pages: 1390 - 1401
      Abstract: Delineating individual tree crowns (ITCs) in high-spatial-resolution images can help to improve forest inventory and management. However, single-band watershed segmentation methods often fail to delineate broadleaf species, particularly in uneven-aged stands when a single-scale parameter is used to fit segments to reference crowns of different sizes. In this study, we present multispectral watershed segmentation and multiscale fitting method for ITC delineation, and the method involves two steps: 1) multispectral watershed segmentation to produce multiscale segmentation for subsequent fitting, which takes full advantage of boundary information contained in the spectral contrast of multiple bands; and 2) multiscale fitting to identify optimal parameters to best fit each ITC, rather than selecting a single parameter value based on its overall fit to the image as a whole. We evaluate the effectiveness of the proposed method using two multispectral images from a mixed broadleaf forest in Central Ontario, Canada. Our results show that multispectral watershed segmentation at multiple spatial scales produces ITC maps of higher quality than the commonly used multiresolution segmentation method. The automated multiscale fitting produces ITC maps of higher quality than the best single-scale segmentation.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Fractal Dimension of Irregular Region of Interest Application to Corn
           Phenology Characterization
    • Authors: Yonglin Shen;Xiuguo Liu;Xiaohui Yuan;
      Pages: 1402 - 1412
      Abstract: Analysis of multitemporal remote sensing imagery offers a reliable and cost-effective means for monitoring crops on a broad-scale and provides consistent temporal measurements. Fractal geometry has been used as a quantitative description of spatial complexity of remote sensing images. Yet, corn field of spatial irregularity alters the fractal dimension of the landscape, which shall be suppressed in the estimation. In this paper, we propose a method for computing fractal dimension from irregular region of interests that minimizes the contribution from 2-D spatial irregularity. Our method was evaluated with normalized difference vegetation index products derived from moderate resolution imaging spectroradiometer and satellite pour l’observation de la terre VEGETATION sensors from three states in the U.S. The experimental results using the time series demonstrated that our proposed fractal dimension estimation method exhibited great consistency and invariance to the change of image spectral characteristics, spatial resolution, and the degree of pixel mixing. In contrast to entropy and variance, the spectral characteristics of different imaging devices exhibited lower impact to the fractal dimension, which also implies its scale invariance. With respect to the detection rate of the first peak, fractal dimension achieved the best consistency. The proposed method for computing fractal dimension provides a critical and reliable measure for studying phenological patterns.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Semantic Classification of Urban Trees Using Very High Resolution
           Satellite Imagery
    • Authors: Dawei Wen;Xin Huang;Hui Liu;Wenzhi Liao;Liangpei Zhang;
      Pages: 1413 - 1424
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Improved Signal Reconstruction Algorithm for Multichannel SAR Based on the
           Doppler Spectrum Estimation
    • Authors: Shao-Shan Zuo;Mengdao Xing;Xiang-Gen Xia;Guang-Cai Sun;
      Pages: 1425 - 1442
      Abstract: For high-resolution wide-swath synthetic aperture radar imaging algorithms, signal reconstruction is a key step. The steering vector plays an important role in signal reconstruction, which can be constructed by the ambiguity components. The information of the ambiguity components, e.g., number and index, is usually regarded to be constant and known. However, we find that the information of ambiguity components is always a piecewise function of the baseband frequency. This means that the steering vector cannot be preconstructed accurately and it will negatively affect the signal reconstruction. This paper presents an improved signal reconstruction method based on the Doppler spectrum estimation. The proposed method can estimate the variant ambiguity components to form the steering vector exactly by the Capon estimation. As a result, the method is able to restore the Doppler spectrum entirely and performs well on the noise reduction. Moreover, the baseband Doppler centroid and antenna pattern can be obtained in the proposed method. Simulated data and airborne raw data are processed to validate the algorithm.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Sparsity-Driven SAR Imaging for Highly Maneuvering Ground Target by the
           Combination of Time-Frequency Analysis and Parametric Bayesian Learning
    • Authors: Lei Yang;Lifan Zhao;Song Zhou;Guoan Bi;
      Pages: 1443 - 1455
      Abstract: In this paper, a well-focused synthetic aperture radar (SAR) image for highly maneuvering ground target is formed. For high-resolution SAR imaging, the phase modulation from the maneuverer's high-order movements severely degrades the focusing quality of the target image, if the conventional SAR imaging algorithm under the constant target velocity assumption is used. To deal with this problem, a new SAR ground moving target imaging (GMTIm) algorithm is presented with a two-step strategy to obtain a high-resolution maneuvering target image with highly focused responses. Pseudo Wigner–Ville distribution is first employed to access and compensate for the bulk of the high-order phase. Then, to further enhance the target image quality, the SAR-GMTIm problem is solved by sparse Bayesian learning (SBL), where an accurate phase autofocusing is incorporated for the compensation of the residual high-order phase. A novel time-frequency representation, known as Lv's distribution, is adopted to determine the parametric dictionary used in the SBL processing. To accommodate the possible multiple-target imaging scenario, the intended SAR-GMTIm algorithm is developed in a coarse-to-fine compensation procedure. Finally, both simulated data and real Gotcha data are applied to validate the effectiveness and superiority of the proposed SAR imaging algorithm for ground maneuvering targets.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Polarimetric SAR Feature Extraction With Neighborhood Preservation-Based
           Deep Learning
    • Authors: Hongying Liu;Shuyuan Yang;Shuiping Gou;Dexiang Zhu;Rongfang Wang;Licheng Jiao;
      Pages: 1456 - 1466
      Abstract: As an advanced nonlinear technique, deep learning, which is based on deep neural networks (DNNs), has attracted considerable attentions. In this paper, we propose a novel neighborhood preserved deep neural network (NPDNN) for polarimetric synthetic aperture radar feature extraction and classification. The spatial relation between pixels is exploited by a jointly weighting strategy. Not only the spatial neighbors but also the pixels in the same superpixel are utilized to weight each pixel. This strategy maintains the spatial dependence leading to superior homogeneity of the terrains without extra computational memory. Moreover, a few labeled samples and their nearest neighbors are employed to train the multilayer NPDNN, which preserves the local structure and reduces the number of labeled samples for classification. Experimental results on synthesized and real PolSAR data show that the proposed NPDNN can improve the classification accuracy compared with state-of-the-art DNNs despite a few input samples.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Contributions of C-Band SAR Data and Polarimetric Decompositions to
           Subarctic Boreal Peatland Mapping
    • Authors: Michael A. Merchant;Justin R. Adams;Aaron A. Berg;Jennifer L. Baltzer;William L. Quinton;Laura E. Chasmer;
      Pages: 1467 - 1482
      Abstract: The objective of this paper is to assess the accuracy of C-band synthetic aperture radar (SAR) datasets in mapping peatland types over a region of Canada's subarctic boreal zone. This paper assessed contributions of quad-polarization linear backscatter intensities (σ°HH, σ°HV, σ°VV), image textures, and two polarimetric scattering decompositions: 1) Cloude–Pottier, and 2) Freeman–Durden. Four quad-polarimetric RADADSAT-2 images were studied at incidence angles of 19.4°, 23.1°, 45.8°, and 48.1°. The influence of combining dual-angular information acquired within a short temporal span was also assessed. These C-band SAR data were used to classify peatlands according to isolated flat bogs (bogs), channel fens (fens), raised peat plateaus (plateaus), and forested uplands (uplands) using a supervised support vector machine (SVM) classifier. Numerous classifications were examined to compare the unique contributions of these variables to classification accuracy. Results suggest linear backscatter variables in isolation produce comparable classification results with those of the Freeman–Durden and Cloude–Pottier decompositions. Combining polarimetric decomposition and texture data into classifications with linear backscatter data resulted in only minor (∼1–3%) improvement. Combining classifications from small and large incidence angles (dual-angular) significantly improved classification results over those of a single image. Classification accuracy was the highest for isolated bogs and open water surfaces, whereas fens, uplands, and plateaus had lower accuracies. The highest accuracy classification (84% and kappa coeff-cient of 0.80) used a dual-angular approach, with additional decomposition and texture information. However, it is noted that texture information rarely improved classification results across all tests. This approach identified isolated flat bogs, channel fens, and raised peat plateaus with >76% producer's accuracies.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • SAR Image Registration Using Multiscale Image Patch Features With Sparse
    • Authors: Jianwei Fan;Yan Wu;Ming Li;Wenkai Liang;Qiang Zhang;
      Pages: 1483 - 1493
      Abstract: In this paper, we propose a new image registration method for synthetic aperture radar (SAR) image with multiscale image patch features, in which the sparse representation technique is exploited. Considering the influence of speckle noise on feature extraction, in the proposed method, a spatial correlation strategy based on stationary wavelet transform is adopted to select the reliable feature points from the initial scale invariant feature transform keypoints in the reference image. By introducing multiscale image patch, a new feature descriptor is further designed to describe the attribute domain of feature points for higher discrimination. The corresponding points in the sensed image are established based on the minimum discrepancy criterion calculated by the sparse representation technique. Moreover, the local geometric consistency among a feature point and its nearest neighbor points is employed to remove the mismatches from the tentative matches. Twenty-two pairs of SAR images acquired under various conditions are utilized to validate the effectiveness of the proposed method. Compared with the traditional SAR image registration methods, the results show that the proposed method is competent to improve the registration performance substantially.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Fast Factorized Backprojection Algorithm for One-Stationary Bistatic
           Spotlight Circular SAR Image Formation
    • Authors: Hongtu Xie;Shaoying Shi;Daoxiang An;Guangxue Wang;Guoqian Wang;Hui Xiao;Xiaotao Huang;Zhimin Zhou;Chao Xie;Feng Wang;Qunle Fang;
      Pages: 1494 - 1510
      Abstract: In this paper, a fast factorized backprojection (FFBP) algorithm is proposed for the one-stationary bistatic spotlight circular synthetic aperture radar (OS-BSCSAR) data processing. This method represents the subimages on polar grids in the slant-range plane instead of the ground plane, which can be accurately referenced to the tracks of both transmitter and receiver. It can not only accurately accommodate the complicated circular track including the motion error, scene topographic information, large spatial variances and significant range-azimuth coupling of the echo data, but also improve the imaging efficiency compared with the backprojection (BP) algorithm. First, OS-BSCSAR imaging geometry is provided, and then the bistatic BP algorithm for the OS-BSCSAR imaging is derived to provide a basis for the proposed FFBP algorithm. Second, based on the subaperture imaging model, the polar grids for calculating the subimages are defined, and the sampling requirements for the polar grids are derived from the viewpoint of the bandwidth, which can offer the near-optimum tradeoff between the imaging quality and the imaging efficiency. Finally, implementation and computational burden of the proposed FFBP algorithm is discussed, and then the speed-up factor of the proposed FFBP algorithm with respect to the bistatic BP algorithm is derived. Experiment results are given to prove the correctness of the theory analysis and validity of the proposed FFBP algorithm.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Airport Detection and Aircraft Recognition Based on Two-Layer Saliency
           Model in High Spatial Resolution Remote-Sensing Images
    • Authors: Libao Zhang;Yingying Zhang;
      Pages: 1511 - 1524
      Abstract: Efficient airport detection and aircraft recognition are essential due to the strategic importance of these regions and targets in economic and military construction. In this paper, a novel airport detection and aircraft recognition method that is based on the two-layer visual saliency analysis model and support vector machines (SVMs) is proposed for high-resolution broad-area remote-sensing images. In the first layer saliency (FLS) model, we introduce a spatial-frequency visual saliency analysis algorithm that is based on a CIE Lab color space to reduce the interference of backgrounds and efficiently detect well-defined airport regions in broad-area remote-sensing images. In the second layer saliency model, we propose a saliency analysis strategy that is based on an edge feature preserving wavelet transform and high-frequency wavelet coefficient reconstruction to complete the pre-extraction of aircraft candidates from airport regions that are detected by the FLS and crudely extract as many aircraft candidates as possible for additional classification in detected airport regions. Then, we utilize feature descriptors that are based on a dense SIFT and Hu moment to accurately describe these features of the aircraft candidates. Finally, these object features are inputted to the SVM, and the aircraft are recognized. The experimental results indicate that the proposed method not only reliably and effectively detects targets in high-resolution broad-area remote-sensing images but also produces more robust results in complex scenes.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Cause, Effect, and Correction of Field Spectroradiometer Interchannel
           Radiometric Steps
    • Authors: Andreas Hueni;Agnieszka Bialek;
      Pages: 1542 - 1551
      Abstract: Field spectroradiometers are often comprised of several spectral detectors to sample the full range of reflected solar irradiance. An example of such an instrument is the Analytical Spectral Devices (ASD) full-range spectroradiometer, featuring three spectral detectors to capture spectra between 350 and 2500 nm. The resulting spectra often exhibit radiometric steps at the joints of these detectors. This study investigates the influence of external temperature and humidity on the magnitude of these steps by experiments based on a climate chamber. Relative radiometric errors at the detector borders were found to reach up to 16% for the visible and near infrared and 21% for the shortwave infrared 2 (SWIR2), whereas relative reflectance errors are target dependent, typically ranging between 2% and 6%. The derived sensor model provides a physically based explanation of the changes in radiometry due to temperature and demonstrates that all spectral bands are affected to a higher or lesser degree. The model can be used to correct for the effect of temperature on the recorded radiances. Applying the model to ASD instruments that were not tested in the climate chamber still leads to reasonable correction results with RMSE values of 0.6%.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Dimensionality Reduction for Hyperspectral Data Based on Pairwise
           Constraint Discriminative Analysis and Nonnegative Sparse Divergence
    • Authors: Xuesong Wang;Yi Kong;Yang Gao;Yuhu Cheng;
      Pages: 1552 - 1562
      Abstract: To improve the classification accuracy of unlabeled large-scale hyperspectral data, a dimensionality reduction algorithm based on pairwise constraint discriminant analysis and nonnegative sparse divergence (PCDA-NSD) is proposed by using the feature transfer learning technology. Different from labeled sample information that is relatively difficult to acquire, pairwise constraints are a kind of useful supervision information, which can be automatically acquired without artificial interference and thus can better avoid the selection of redundant and noisy samples. Therefore, the pairwise constraint discriminant analysis method is used to learn potential discriminant information of sample sets in the source and target domains. Consequently, positively correlated constraint samples in the source and target domains share one subspace whereas positively and negatively correlated constraint samples are highly separated. Because hyperspectral data in the source and target domains often follow different distributions, a nonnegative sparse divergence is established to measure the divergence between different distributions, based on the nonnegative sparse representation method. Therefore, not only the computation load of the kernel matrix is reduced, but also the natural discriminant capacity is obtained. Experiments of a four-group hyperspectral data show that PCDA-NSD can reduce dimensionality of target data and improve classification accuracy and efficiency by adequate use of the information available in similar hyperspectral data.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Ideal Regularized Composite Kernel for Hyperspectral Image Classification
    • Authors: Jiangtao Peng;Hong Chen;Yicong Zhou;Luoqing Li;
      Pages: 1563 - 1574
      Abstract: This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral image (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of the ideal kernel according to a regularization kernel learning framework, which captures both the sample similarity and label similarity and makes the resulting kernel more appropriate for specific HSI classification tasks. With the ideal regularization, the kernel learning problem has a simple analytical solution and is very easy to implement. The ideal regularization can be used to improve and to refine state-of-the-art kernels, including spectral kernels, spatial kernels, and spectral-spatial composite kernels. The effectiveness of the proposed IRCK is validated on three benchmark hyperspectral datasets. Experimental results show the superiority of our IRCK method over the classical kernel methods and state-of-the-art HSI classification methods.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Region-Based Structure Preserving Nonnegative Matrix Factorization for
           Hyperspectral Unmixing
    • Authors: Lei Tong;Jun Zhou;Xue Li;Yuntao Qian;Yongsheng Gao;
      Pages: 1575 - 1588
      Abstract: Hyperspectral unmixing is one of the most important techniques in the remote sensing image analysis. In recent years, the nonnegative matrix factorization (NMF) method is widely used in hyperspectral unmixing. In order to solve the nonconvex problem of the NMF method, a number of constraints have been introduced into NMF models, including sparsity, manifold, smoothness, etc. However, these constraints ignore an important property of a hyperspectral image, i.e., the spectral responses in a homogeneous region are similar at each pixel but vary in different homogeneous regions. In this paper, we introduce a novel region-based structure preserving NMF (R-NMF) to explore consistent data distribution in the same region while discriminating different data structures across regions in the unmixed data. In this method, a graph cut algorithm is first applied to segment the hyperspectral image to small homogeneous regions. Then, two constraints are applied to the unmixing model, which preserve the structural consistency within the region while discriminating the differences between regions. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-the-art unmixing approaches.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral
           Imagery: A Quantitative Assessment
    • Authors: Xiong Xu;Xiaohua Tong;Antonio Plaza;Yanfei Zhong;Huan Xie;Liangpei Zhang;
      Pages: 1589 - 1600
      Abstract: Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been obvious that the utilized abundance map has a strong impact on the subsequent subpixel mapping procedure. However, limited attention has been given to the impact of the different aspects in the spectral unmixing model on the subpixel mapping performance. In this paper, a detailed quantitative assessment of different aspects in linear spectral mixture analysis, such as the criteria used to determine the types of pixels, the abundance sum-to-one constraint in the unmixing, and the accuracy of the utilized abundance maps, is investigated. This is accomplished by designing an experimental procedure with replaceable components. A total of six hyperspectral images (four synthetic and two real) were utilized in our experiments. By investigating these critical issues, we can further improve the performance of subpixel mapping techniques.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Hyperspectral Image Classification With Rotation Random Forest Via KPCA
    • Authors: Junshi Xia;Nicola Falco;Jón Atli Benediktsson;Peijun Du;Jocelyn Chanussot;
      Pages: 1601 - 1609
      Abstract: Random Forest (RF) is a widely used classifier to show a good performance of hyperspectral data classification. However, such performance could be improved by increasing the diversity that characterizes the ensemble architecture. In this paper, we propose a novel ensemble approach, namely rotation random forest via kernel principal component analysis (RoRF-KPCA). In particular, the original feature space is first randomly split into several subsets, and KPCA is performed on each subset to extract high order statistics. The obtained feature sets are merged and used as input to an RF classifier. Finally, the results achieved at each step are fused by a majority vote. Experimental analysis is conducted using real hyperspectral remote sensing images to evaluate the performance of the proposed method in comparison with RF, rotation forest, support vector machines, and RoRF-PCA. The obtained results demonstrate the effectiveness of the proposed method.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • A Novel Endmember Extraction Method for Hyperspectral Imagery Based on
           Quantum-Behaved Particle Swarm Optimization
    • Authors: Rong Liu;Liangpei Zhang;Bo Du;
      Pages: 1610 - 1631
      Abstract: Endmember extraction is one of the most important issues in hyperspectral image analysis. Under the linear mixing model and pure pixel assumption, a number of convex-geometry-based methods have been developed in the past decades. However, these traditional methods generally produce unsatisfactory results since they require the hyperspectral image to have a convex structure and this is not exactly the case with the real image scene. The particle swarm optimization (PSO) algorithm has recently been employed to address the endmember extraction problem, but its performance is limited by the premature convergence and lower precision of the standard PSO, and much effort is required to enhance the optimization result. To address these problems, in this study, a novel quantum-behaved particle swarm optimization (QPSO) algorithm is proposed for hyperspectral endmember extraction. The notable advantages of the proposed method include: 1) a row–column coding approach for the particles is designed to accelerate the optimization process; 2) a cooperative approach is employed to update the particles’ individual and global best positions, in order to help the particles’ optimization behavior in the multidimensional search space; and 3) two kinds of objective functions, namely, maximizing the simplex volume formed by the endmember combination, and minimizing the root-mean-square error between the original image and its remixed image, are incorporated in a sequential optimization approach for the endmember extraction problem, which makes the algorithm robust to outliers at an acceptable time cost. The extensive experimental results prove that QPSO is able to find the optimal endmember combination.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Superpixel Tensor Sparse Coding for Structural Hyperspectral Image
    • Authors: Zhixi Feng;Min Wang;Shuyuan Yang;Zhi Liu;Linzan Liu;Bin Wu;Hong Li;
      Pages: 1632 - 1639
      Abstract: In this paper, a superpixel tensor sparse coding (STSC) based hyperspectral image classification (HIC) method is proposed, by exploring the high-order structure of hyperspectral image and utilizing information along all dimensions to better understand data. First, a hierarchical spatial affinity propagation algorithm is developed to rapidly cluster the image into multiple superpixels tensors. Then, a new STSC-based classifier followed by hybrid pixel-superpixel ensemble strategy is constructed for HIC. Because superpixels can reduce the misclassification caused by mixed pixel and tensor sparse coding can simultaneously classify multiple superpixels, rapid and accurate HIC can be achieved. Some experiments are taken on several datasets, and the results show the superiority of STSC to its counterparts in terms of speed and accuracy.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Ensemble Learning From Synthetically Mixed Training Data for Quantifying
           Urban Land Cover With Support Vector Regression
    • Authors: Akpona Okujeni;Sebastian van der Linden;Stefan Suess;Patrick Hostert;
      Pages: 1640 - 1650
      Abstract: Generating synthetically mixed data from library spectra provides a direct means to train empirical regression models for subpixel mapping. In order to best represent the subpixel composition of image data, the generation of synthetic mixtures must incorporate a multitude of mixing possibilities. This can lead to an excessive amount of training samples. We show that increasing mixing complexity in the training set improves model performance when quantifying urban land cover with support vector regression (SVR). To cope with the challenging increase in the number of training samples, we propose the use of ensemble learning based on bootstrap aggregation from synthetically mixed training data. The workflow is tested on simulated spaceborne imaging spectrometer data acquired over Berlin, Germany. Comparisons to SVR without bagging and multiple endmember spectral mixture analysis reveal the usefulness of the methodology for quantitative urban mapping.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • A Bayesian-Network-Based Classification Method Integrating Airborne LiDAR
           Data With Optical Images
    • Authors: Zhizhong Kang;Juntao Yang;Ruofei Zhong;
      Pages: 1651 - 1661
      Abstract: Point cloud classification is of great importance to applications of airborne Light Detection And Ranging (LiDAR) data. In recent years, airborne LiDAR has been integrated with various other sensors, e.g., optical imaging sensors, and thus, the fusion of multiple data types for scene classification has become a hot topic. Therefore, this paper proposes a Bayesian network (BN) model that is suitable for airborne point cloud classification fusing multiple data types. Based on an analysis of the characteristics of LiDAR point clouds and aerial images, we first extract the geometric features from the point clouds and the spectral features from the optical images. The optimal BN structure is then trained using an improved mutual-information-based K2 algorithm to obtain the optimal BN classifier for point cloud classification. Experiments demonstrate that the BN classifier can effectively distinguish four types of basic ground objects, including ground, vegetation, trees, and buildings, with a high accuracy of over 90%. Moreover, compared with other classifiers, the proposed BN classifier can achieve the highest overall accuracies, and in particular, the classifier demonstrates its advantage in the classification of ground and low vegetation points.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
  • Introducing IEEE collabratec
    • Pages: 1662 - 1662
      Abstract: Advertisement, IEEE.
      PubDate: April 2017
      Issue No: Vol. 10, No. 4 (2017)
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