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  Subjects -> ELECTRONICS (Total: 155 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 4)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Microelectronic Engineering     Open Access   (Followers: 9)
Advances in Power Electronics     Open Access   (Followers: 17)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 233)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 19)
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: 25)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 29)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 9)
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: 38)
China Communications     Full-text available via subscription   (Followers: 7)
Circuits and Systems     Open Access   (Followers: 12)
Consumer Electronics Times     Open Access   (Followers: 6)
Control Systems     Hybrid Journal   (Followers: 84)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 68)
Electronic Markets     Hybrid Journal   (Followers: 7)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 50)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 8)
Electronics For You     Partially Free   (Followers: 53)
Electronics Letters     Hybrid Journal   (Followers: 23)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 38)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 2)
Energy Storage Materials     Full-text available via subscription  
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: 153)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 3)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 44)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 36)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 26)
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: 41)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 36)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 45)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 13)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 27)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 11)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 18)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 47)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 5)
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: 12)
IET Power Electronics     Hybrid Journal   (Followers: 23)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 15)
IETE Journal of Education     Open Access   (Followers: 3)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 7)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 26)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 6)
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: 5)
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: 14)
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: 10)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 7)
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: 2)
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: 4)
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: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 6)
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: 13)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 6)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 4)
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: 113)
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: 7)
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: 18)
Journal of Signal and Information Processing     Open Access   (Followers: 8)
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 13)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 6)
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: 31)
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: 12)
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: 3)
Security and Communication Networks     Hybrid Journal   (Followers: 3)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 43)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 52)
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]   [43 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • Information for Authors
    • Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Institutional Listings
    • Abstract: Presents institutional listings relating to this publication.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Frontcover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • IEEE Geoscience and Remote Sensing Society
    • Abstract: Provides a listing of the editorial board, current staff, committee members and society officers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging
    • Authors: Uta Heiden;Akira Iwasaki;Andreas Müller;Martin Schlerf;Thomas Udelhoven;Kuniaki Uto;Naoto Yokoya;Jocelyn Chanussot;
      Pages: 3904 - 3908
      Abstract: In 2015, two major conferences focused on hyperspectral remote sensing and imaging spectroscopy were held in Luxembourg and Tokyo, respectively. They are namely the 9th EARSeL SIG Imaging Spectroscopy workshop and the 7th Workshop on Hyperspectral Image and Signal Processing. As a follow up to these conferences, the current special issue of JSTARS was launched and we are proud to present a selection of 48 excellent papers. The large number of papers, which were drawn from both extended versions of workshop presentations and traditional paper submissions, is indicative of the high level of research activity, interest, and applications in this field. The papers in this special issue cover a wide range of topics, including new imaging spectroscopy systems, research and applications of imaging spectroscopy, and advances in hyperspectral image and signal processing.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Development of a Low-Cost Hyperspectral Whiskbroom Imager Using an Optical
           Fiber Bundle, a Swing Mirror, and Compact Spectrometers
    • Authors: Kuniaki Uto;Haruyuki Seki;Genya Saito;Yukio Kosugi;Teruhisa Komatsu;
      Pages: 3909 - 3925
      Abstract: Both inductive and deductive approaches based on hyperspectral remote sensing data require abundant observation opportunities under various conditions due to the high dimensionality of the hyperspectral data. With the recent advent of low-cost lightweight unmanned aerial vehicles (UAVs), UAVs for low-altitude aerial observation are becoming commodities rather than special equipment. Therefore, the appearance of low-cost hyperspectral imagers is anticipated for aerial hyperspectral sensing via UAVs. In this paper, we describe the development of a low-cost hyperspectral imager based on a whiskbroom scanning mechanism. The main components of the developed system include an optical fiber bundle, a swing mirror, and compact spectrometers. An image formed by an objective lens is quantized into a set of pixels by a two-dimensional array of quartz fiber-optic cables at one end of an optical fiber bundle. The quantized image travels to the other end of the bundle, inside of which a swing mirror is used for cross-track scanning. The light in each pixel of the quantized image is then measured using a compact spectrometer. Calculated reflectances in close-range measurements of color checkered patterns were spatially and spectrally accurate. In an aerial measurement of a coastal area from a 20-m altitude via a lightweight UAV, a hyperspectral image with a 0.5-m spatial resolution and an 8-m swath was acquired. Based on pattern matching using cross correlation, classification of three classes of marine macrophyte beds, agar, coralline, and sand realized overall accuracies of 0.755 (diffuse dominant illumination) and 0.719 (direct sunlight dominant illumination).
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Ground-Based Panoramic Stereo Hyperspectral Imaging System With Multiband
           Stereo Matching
    • Authors: Ali Can Karaca;Alp Ertürk;Mehmet Kemal Güllü;Sarp Ertürk;
      Pages: 3926 - 3940
      Abstract: This paper presents a novel panoramic hyperspectral stereo imaging system with depth map estimation capability. Images are collected from two line scan hyperspectral cameras in stereo configuration, while the platform is rotating. The proposed system combines the strengths of hyperspectral, stereo, and panoramic imaging. In addition, the proposed system uses passive sensors and hence does not need any radiation source, unlike LiDAR-based systems. In this paper, the proposed system, which captures hyperspectral data from 400 to 1000 nm wavelengths, is introduced, the stereo calibration procedure is described, corresponding analytic analyses are carried out with relation to rotating line-scan camera theory, and critical system parameters and aspects are addressed. In addition, a novel multiband stereo matching approach, which introduces multiband Census transform and SAD-based cost aggregation to stereo matching literature, is proposed. Experimental results are evaluated using two hyperspectral datasets, and the performance of the proposed algorithm is compared with the performance of several prominent matching algorithms available in the literature. A LiDAR-based system is used to enable an analysis of depth information accuracy. The experimental results show the enhanced matching and depth estimation performance of the proposed matching approach over standard stereo methods, and the consistency of depth values between LiDAR and the proposed system. The proposed system, which combines the strengths of panoramic imaging and stereo imaging with the high spectral resolution of hyperspectral cameras, uses passive imaging solutions and is fit to be used in applications requiring depth computation, target detection, and change detection.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Mapping Fractional Land Use and Land Cover in a Monsoon Region: The
           Effects of Data Processing Options
    • Authors: Bumsuk Seo;Christina Bogner;Thomas Koellner;Björn Reineking;
      Pages: 3941 - 3956
      Abstract: Existing global land use/land cover (LULC) raster maps have limited spatial and thematic resolution relative to the strong heterogeneity of agricultural landscapes. One promising approach to derive more informative maps is using fractional cover instead of hard classification. Here, we evaluate the effect of three key data processing options on the performance of random forest (RF) fractional cover models for moderate resolution imaging spectroradiometer (MODIS) data in a heterogeneous agricultural landscape in a monsoon region: 1) selection of spectral predictor sets [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), surface reflectance (SR), and all combined (Full)]; 2) time interval (8-day vs. 16-day); and 3) smoothing (no smoothing versus Savitzky–Golay (SG) filter). Model performance was assessed with spatially stratified root-mean-square error (RMSE), Spearman’s rank correlation, and R^2 , per LULC type and averaged over all types. We found adequate performance of the best model (avg. \rho= 0.62 ) that used all predictors, 8-day interval and no smoothing. Among the different alternatives, the choice of predictors accounted for 36.3% of the variation, smoothing for 19.0%, and time interval for 17.9%. The intrinsic dimensionalities of the spectral predictors were investigated to complement the variable importance analyses. Although predicting LULC fractions for minor types remained difficult, our results suggest that existing satellite products can be a useful source of information about LULC at subpixel level provided the data-processing options are properly chosen.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Discriminating Rangeland Management Practices Using Simulated HyspIRI,
           Landsat 8 OLI, Sentinel 2 MSI, and VENµS Spectral Data
    • Authors: Mbulisi Sibanda;Onisimo Mutanga;Mathieu Rouget;
      Pages: 3957 - 3969
      Abstract: This study sought to spectrally discriminate grasses grown under different management practices (i.e., mowing, grazing, fertilizer application, and burning), using field spectrometer data resampled to Hyperspectral Infrared Imager (HyspIRI), Landsat 8 Operational Land Imager (OLI), Sentinel 2 Multispectral Instrument (MSI), and Vegetation and Environment monitoring on a new MicroSatellite (Venμs). The study is inspired by the long standing challenge of lack of suitable satellite data with high temporal, spectral, and spatial resolutions for rangelands monitoring. Specifically, this study spectrally discriminated grasses grown under 1) different rangeland management practices as well as 2) different levels of application of each practice. Results of this study show that the spectral setup of HyspIRI, Sentinel 2 MSI, Venus, and Landsat 8 OLI yielded high accuracies of up to 92%, 82%, 83%, and 75% overall accuracy, respectively, in discriminating grass grown under different rangeland management practices. The high classification accuracies were exhibited by the use of vegetation indices and wavebands located in the red edge (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: bands 5, 6, and 7 8a) and NIR (HyspIRI: 700, 740, and 780 nm and Sentinel 2 MSI: band 8a) spectra, respectively. Results of this study illustrate that although simulated Sentinel 2 MSI data yields lower classification accuracies when compared to HyspIRI, it offers better classification accuracies with high agreements between training and testing datasets when compared to the HyspIRI data. Overall, the findings of this study underscore the potential of upcoming satellite missions in ensuring informed rangeland monitoring and management applications.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • ${text{DA}}_{{text{NIR}}}$ )—A+Novel+Approach+for+Green+Vegetation+Fraction+Estimation+using+Field+Hyperspectral+Data&rft.title=Selected+Topics+in+Applied+Earth+Observations+and+Remote+Sensing,+IEEE+Journal+of&rft.issn=1939-1404&;&rft.aufirst=Dibyendu&;Prabir+Kumar+Das;Kazi+Arif+Alam;P.+Safwan;Soubhik+Paul;Manoj+Kumar+Nanda;Vinay+Kumar+Dadhwal;">Delta Area at Near Infrared Region ( ${text{DA}}_{{text{NIR}}}$ )—A
           Novel Approach for Green Vegetation Fraction Estimation using Field
           Hyperspectral Data
    • Authors: Dibyendu Dutta;Prabir Kumar Das;Kazi Arif Alam;P. Safwan;Soubhik Paul;Manoj Kumar Nanda;Vinay Kumar Dadhwal;
      Pages: 3970 - 3981
      Abstract: A new metric called “Delta Area at Near Infrared Region” ( {text{DA}}_{{text{NIR}}} ) has been conceptualized and implemented for estimation of green vegetation fraction (GVF) using field spectroradiometer at different growth stages of potato over two consecutive potato growing seasons (2012–2013 and 2013–2014). Vertical photograph, collocated in time and space with spectroradiometer observation, was acquired and digitally classified for GVF. While comparing with other conventional indices, {text{DA}}_{{text{NIR}}} showed linearity at higher GVF values and capable of capturing the movement of the curve caused by soil-vegetation mixture between inflection point and near infrared peak. Among all the univariate models, {text{DA}}_{{text{NIR}}} showed the highest accuracy with {text{R}}^{2}=0.94 and {text{RMSE}}=7.0 . The new index also exhibited the highest sensitivity for the entire range of GVF while comparing with other indices; however, the sensitivity decreases at higher values especially above 70%. Stepwise multiple linear regression (SMLR) and partial least squares regression (PLSR) were performed using all the spectral variables. The prediction accuracy was further improved over univariate analysis wherein PLSR was able to predict the vegetation fraction with the highest accuracy ( {text{R}}^{2}=0.94 and {text{RMSE}}=5.32 ). In both SMLR and PLSR, ${text{DA}}_{{text{NIR}}}$ contributed significantly to improve the estimation accuracy. These findings suggest that {text{DA}}_{{text{NIR}}} can be used as a surrogate indicator of GVF independently or in combination with other vegetation indices to further improve the estimation accuracy.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Utilizing a PLSR-Based Band-Selection Procedure for Spectral Feature
           Characterization of Floristic Gradients
    • Authors: Carsten Neumann;Michael Förster;Birgit Kleinschmit;Sibylle Itzerott;
      Pages: 3982 - 3996
      Abstract: The study introduces a new approach for the characterization of floristic gradients by hyperspectral features in a partial least squares regression (PLSR) framework. As ecological factors influence the composition of vegetation, our study is aimed to reveal related effects on spectral signatures. For this purpose, the variation of plant species in an open dryland area was projected into a three-dimensional ordination space using nonmetric multidimensional scaling (NMDS). Subsequently, ordination axes score rotations were performed in text{180}^\circ semicircles and the waveband-specific correlation to spectral field measurements of reflectance, continuum removed, and first-derivative spectra were extracted. A bootstrapped PLSR modeling was applied over the entire rotation space using varying numbers of correlated spectral variables as input samples. On that basis, a new PLSR model suitability term was defined by isosurfaces that are spanned over ordination regions where PLSR latent vector (LV) number and PLSR text{R}^2 variance is minimized. It incorporates model performance evaluation with feature characterization using weighted frequencies of spectral variable input in suitable ordination areas. Final PLSR suitability surfaces were transferred to image spectra to prove feature stability and model performance. Our investigation supports the assumption that spectral features are separable to distinct ordination space regions that can be related to individual species gradients. Thereby, the selection of an optimal PLSR model crucially depends on the spectral transformation technique. We further show that stable PLSR models can be derived in multiple ordination directions whereby an appropriate variable selection using suitability surface optimization reduces feature mismatch between field and image spectra.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a
           Semiarid Environment
    • Authors: Anita D. Bayer;Martin Bachmann;Derek Rogge;Andreas Müller;Hermann Kaufmann;
      Pages: 3997 - 4010
      Abstract: Semiarid regions are especially vulnerable to climate change and human-induced land-use changes and are of major importance in the context of necessary carbon sequestration and ongoing land degradation. Topsoil properties, such as soil carbon content, provide valuable indicators to these processes, and can be mapped using imaging spectroscopy (IS). In semiarid regions, this poses difficulties because models are needed that can cope with varying land surface and soil conditions, consider a partial vegetation coverage, and deal with usually low soil organic carbon (SOC) contents. We present an approach that aims at addressing these difficulties by using a combination of field and IS to map SOC in an extensively used semiarid ecosystem. In hyperspectral imagery of the HyMap sensor, the influence of nonsoil materials, i.e., vegetation, on the spectral signature of soil dominated image pixels was reduced and a residual soil signature was calculated. The proposed approach allowed this procedure up to a vegetation coverage of 40% clearly extending the mapping capability. SOC quantities are predicted by applying a spectral feature-based SOC prediction model to image data of residual soil spectra. With this approach, we could significantly increase the spatial extent for which SOC could be predicted with a minimal influence of a vegetation signal compared to previous approaches where the considered area was limited to a maximum of, e.g., 10% vegetation coverage. As a regional example, the approach was applied to a {320;text{km}}^{2} area in the Albany Thicket Biome, South Africa, where land cover and land-use changes have occurred due to decades of unsustainable land management. In the generated maps, spatial SOC patterns were interpreted and linked to geomorphic features and land surface processes, i.e., areas of soil erosion. It was found that the hosen approach supported the extraction of soil-related spectral image information in the semiarid region with highly varying land cover. However, the quantitative prediction of SOC contents revealed a lack in absolute accuracy.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Quantification of Soil Variables in a Heterogeneous Soil Region With
           VIS–NIR–SWIR Data Using Different Statistical Sampling and Modeling
    • Authors: Michael Vohland;Monika Harbich;Marie Ludwig;Christoph Emmerling;Sören Thiele-Bruhn;
      Pages: 4011 - 4021
      Abstract: Estimation accuracies obtained for soil properties from spectroradiometer data markedly depend on the individual sample set. The choice of the statistical method to sample a calibration set and the extension of the multivariate modeling approach with bagging and/or spectral variable selection may optimize predictions. We studied this with a set of 172 arable topsoils from a region near Trier (Germany) that covered—as often typical for medium to large-scale applications of soil spectroscopy—a wide range of different soil situations. Yet, differences concerning target variables—organic carbon (OC), nitrogen (N), microbial biomass ( {\rm{C}_{{{\rm mic}}}} ) and thermostable carbon ( {\rm{C}_{{{\rm \i\nert}}}} )—were small. Based on a split of calibration and validation data with the Kennard–Stone algorithm, we found only moderate improvements towards partial least squares regression (PLSR) when combining PLSR with bagging and, for spectral variable selection, with “competitive adaptive reweighted sampling” (CARS). R2 improved for OC (from 0.75 to 0.79), N (from 0.72 to 0.77) and {\rm{C}_{{{\rm \i\nert}}}} (from 0.66 to 0.68) in the validation. Additionally, we used individual calibration sets for each validation sample. In this “local” approach, we clustered calibration samples in the spectral feature space and selected individually the most similar sample from each cluster. Combining bagging-CARS-PLSR with this local approach improved R2 markedly to 0.76 for {\rm{C}_{{{\rm \i\nert}}}} , and slightly to 0.82 for OC and to 0.76 (previously 0.73) for {\rm{C}_{{{\rm mic}}}} . Effects of the local approach were twofold, as it removed improper samples from the calibration and balanced skewness in the data distribution.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Evaluation of the Quasi-Analytical Algorithm (QAA) for Estimating Total
           Absorption Coefficient of Turbid Inland Waters in Northeast China
    • Authors: Sijia Li;Kaishan Song;Guangyi Mu;Ying Zhao;Jianhang Ma;Jianhua Ren;
      Pages: 4022 - 4036
      Abstract: Total light absorption plays a vital role in aquatic ecosystem. In this research, the performance of quasi-analytical algorithm (QAA) was evaluated for deriving water total absorption coefficient $a(lambda)$, from remote sensing reflectance $text{R}_text{rs}(lambda)$, using in situ data sets collected in typically turbid waters including Chagan Lake, Songhua Lake, and Shitoukoumen and Xinlicheng reservoirs in Northeast China. A better performance of algorithm was observed when the calculation parameter of reference wavelength was shifted from 670 nm to 700 or 740 nm. In particular, the accuracy of estimated absorption coefficients at 550 and 675 nm for Songhua Lake in the summer was much improved when using 700 nm as reference band. In general, the QAA typically underestimated $a_text{nw}(lambda)$ (nonwater absorption) for all the waters in different seasons with regression slope lower than one especially at 400 nm. However, longer reference wavelength leads to more uncertainties in deriving $a_text{nw}(lambda)$ with higher root-mean-square error (RMSE) and absolute percentage difference (APD) at 440 nm. It was found that if the average contribution of chromophoric dissolved organic matter (CDOM) absorption at 440 nm dominates over the nonwater absorption, QAA achieved a better predicted result. Under the premise of that, the larger the contribution rate of particulate absorption at 440 nm, the poorer the predicted results were derived. The relationship could be established between $a_text{nw}(lambda)$ and $text{R}_text{rs}(lambda)$ through QAA algorithm, but it is not an optimal algorithm for turbid case 2 waters and large amounts of in situ data sets in different seasons are needed to calibrate the algorithm to achieve better performances.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • First Suomi NPP Cal/Val Campaign: Intercomparison of Satellite and
           Aircraft Sounding Retrievals
    • Authors: Daniel K. Zhou;Xu Liu;Allen M. Larar;Jialin Tian;William L. Smith;Susan H. Kizer;Wan Wu;Quanhua Liu;Mitch D. Goldberg;
      Pages: 4037 - 4046
      Abstract: Satellite ultraspectral infrared sensors provide key data records essential for weather forecasting and climate change science. The Suomi National Polar-orbiting Partnership (NPP) satellite environmental data records (EDRs) are retrieved from calibrated ultraspectral radiance or sensor data records (SDRs). Understanding the accuracy of retrieved EDRs is critical. The first Suomi NPP Calibration/Validation Campaign was conducted during May 2013. The NASA high-altitude ER-2 aircraft carrying ultraspectral interferometer sounders such as the National Airborne Sounder Testbed-Interferometer (NAST-I) flew under the Suomi NPP satellite that carries the cross-track infrared sounder (CrIS) and the advanced technology microwave sounder (ATMS). Here, we intercompare the EDRs produced with different retrieval algorithms from SDRs measured from satellite and aircraft. The available dropsonde and radiosonde measurements together with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis are used to assess the results of this experiment. This study indicates that the CrIS/ATMS retrieval accuracy meets the Suomi NPP EDR requirement, except in the planetary boundary layer (PBL) where we have less confidence in meeting the requirement due to retrieval null-space error.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Wavelet-Domain Multiview Active Learning for Spatial-Spectral
           Hyperspectral Image Classification
    • Authors: Xiong Zhou;Saurabh Prasad;Melba M. Crawford;
      Pages: 4047 - 4059
      Abstract: Active learning (AL) has been shown to be effective for strategic selection of training samples to support classification of hyperspectral imagery. It is well understood that the performance of classification can further be improved by utilizing the spatial information in hyperspectral images. In this paper, we propose a new wavelet-based multiview AL approach for hyperspectral image classification. Specifically, a three-dimensional redundant wavelet transform (3D-RDWT) is used to generate multiple views that are then integrated in a multiview AL framework. The spatial features generated via 3D-RDWT not only provide sufficient views for multiview AL, but are also less sensitive to additive noise. Within this framework, we also propose new query criteria that result in effective AL. An intersection-based query criterion is proposed to reduce the redundancy within the contention pool. A singularity-based criterion is also used to identify informative pixels by taking spatial information into account when selecting samples. The proposed method is evaluated on four real-world hyperspectral datasets, and the experimental results demonstrate the efficacy of the proposed method compared with traditional AL methods.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Combining Rotation Forest and Multiscale Segmentation for the
           Classification of Hyperspectral Data
    • Authors: Jike Chen;Junshi Xia;Peijun Du;Jocelyn Chanussot;
      Pages: 4060 - 4072
      Abstract: In order to tackle two key issues, e.g., high-accuracy classifier and spatial information model, in the classification of hyperspectral images, a new classification scheme, which combines two powerful techniques, i.e., rotation forest (RoF) and multiscale (MS) segmentation, is proposed in this paper. MS segmentation is used to obtain the spatial information from different levels. Then, the objects produced by MS segmentation are treated as the input of the RoF classifier. Furthermore, multiple classification results generated by the RoF classifiers and MS segmentation are combined using a majority voting rule to generate the final result. Experimental results on two real hyperspectral datasets demonstrate that the proposed method performs particularly well in terms of overall and class-specific accuracies and generates the classification map with much more homogeneous regions than traditional methods. Moreover, the impacts of parameters on the classification performances are also analyzed.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Spectral–Spatial Classification of Hyperspectral Image Based on
           Deep Auto-Encoder
    • Authors: Xiaorui Ma;Hongyu Wang;Jie Geng;
      Pages: 4073 - 4085
      Abstract: Deep learning, which represents data by a hierarchical network, has proven to be efficient in computer vision. To investigate the effect of deep features in hyperspectral image (HSI) classification, this paper focuses on how to extract and utilize deep features in HSI classification framework. First, in order to extract spectral–spatial information, an improved deep network, spatial updated deep auto-encoder (SDAE), is proposed. SDAE, which is an improved deep auto-encoder (DAE), considers sample similarity by adding a regularization term in the energy function, and updates features by integrating contextual information. Second, in order to deal with the small training set using deep features, a collaborative representation-based classification is applied. Moreover, in order to suppress salt-and-pepper noise and smooth the result, we compute the residual of collaborative representation of all samples as a residual matrix, which can be effectively used in a graph-cut-based spatial regularization. The proposed method inherits the advantages of deep learning and has solutions to add spatial information of HSI in the learning network. Using collaborative representation-based classification with deep features makes the proposed classifier extremely robust under a small training set. Extensive experiments demonstrate that the proposed method provides encouraging results compared with some related techniques.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Adaptive Nonlocal Spatial–Spectral Kernel for Hyperspectral Imagery
    • Authors: Jianing Wang;Licheng Jiao;Shuang Wang;Biao Hou;Fang Liu;
      Pages: 4086 - 4101
      Abstract: Window-based technique is an effective and widely used strategy to exploit spatial–spectral features for hyperspectral image (HSI) classification, which demonstrates outstanding performance in recent research works, such as composite kernels (CKs), joint sparse representation classification (JSRC), etc. However, the utilization of square-shaped window is liable to misclassify the pixels located around the boundaries of class and leads to over-smoothed classification performance. In this paper, in order to alleviate this problem and sufficiently utilize the rich spectral signatures, we propose an adaptive nonlocal spatial–spectral kernel (ANSSK) to reflect complex manifolds by simultaneously measuring the similarities of different homogeneous patches in kernel feature space rather than pair-wise similarities in CK methods. Meanwhile, an adaptive nonlocal (AN) strategy is proposed to automatically assign an adaptive threshold for acquiring homogeneous patches based on the estimation of available training samples, which provides a data-driven and local-based strategy for dealing with the problem caused by window-based techniques. Experimental results on three real HSIs demonstrate the effectiveness of the proposed methods, and the proposed ANSSK can be easily embedded with different classifiers, such as support vector machine (SVM), representation-based classifiers, etc.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Bilayer Elastic Net Regression Model for Supervised Spectral-Spatial
           Hyperspectral Image Classification
    • Authors: Bushra Naz Soomro;Liang Xiao;Lili Huang;Shahzad Hyder Soomro;Mohsen Molaei;
      Pages: 4102 - 4116
      Abstract: In this paper, we propose a novel bilayer elastic net (ELN ^2 ) regression model for hyperspectral image (HSI) classification, exploiting the spectral-spatial information. The proposed model is designed to address the special problematic characteristics of HSI, namely, high dimensionality of hyperspectral pixels, limited labeled samples, and spatial variability of spectral signatures. To alleviate these problems, by exploiting the spectral and spatial information, the proposed model features in the following two components: 1) spectral-only elastic net regression in the first layer and 2) spatial contextual driven elastic net regularization in the second layer. In the first layer, to encourage a grouping effect and feature selection, we use multinomial logistic regression (MLR) model penalized by the ELN to optimize the spectral-only classifier parameters for the initial HSI classification. In the second layer, spatial Markov-random-field-based gradient profiles are incorporated into the ELN penalty over the hidden marginal probability of the posterior distribution to encourage the spatial smoothness. Furthermore, the true labels of training samples are fixed as an additional constraint in the second-layer optimizing model to further improve the classification accuracy. Moreover, an effective algorithm named as ELN ^2 _RegMLR is developed by coupling the path-wise coordinate descent algorithm, and variable splitting and augmented Lagrangian approach to solve the proposed model. Experimental results on several popular datasets show that the proposed method outperforms many state-of the-art classifiers in terms of the overall accuracy, average accuracy, statistic coefficient kappa, and visual classification map quality.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Crop Classification Based on Feature Band Set Construction and
           Object-Oriented Approach Using Hyperspectral Images
    • Authors: Xia Zhang;Yanli Sun;Kun Shang;Lifu Zhang;Shudong Wang;
      Pages: 4117 - 4128
      Abstract: Remote sensing plays a significant role for crop classification. Accurate crop classification is a common requirement to precision agriculture, including crop area estimation, crop yield estimation, precision crop management, etc. This paper developed a new crop classification method involving the construction and optimization of the vegetation feature band set (FBS) and combination of FBS and object-oriented classification (OOC) approach. In addition to the spectral and textural features of the original image, 20 spectral indices sensitive to the vegetation's biological parameters are added to the FBS to distinguish specific vegetation. A spectral dimension optimization algorithm of FBS based on class-pair separability (CPS) is also proposed to improve the separability between class pairs while reducing data redundancy. OOC approach is conducted on the optimized FBS based on CPS to reduce the salt-and-pepper noise. The proposed classification method was validated by two airborne hyperspectral images. The first image acquired in an agricultural area of Japan includes seven crop types, and the second image acquired in a rice breeding area consists of six varieties of rice. For the first image, the proposed method distinguished different vegetation with an overall accuracy of 97.84% and kappa coefficient of 0.96. For the second image, the proposed method distinguished the rice varieties accurately, achieving the highest overall accuracy (98.65%) and kappa coefficient (0.98). Results demonstrate that the proposed method can significantly improve crop classification accuracy and reduce edge effects, and that textural features combined with spectral indices sensitive to the chlorophyll, carotenoid, and Anthocyanin indicators contribute significantly to crop classification. Therefore, it is an effective approach for classifying crop species, monitoring invasive species, as well as precision agriculture related appl-cations.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Spatial–Spectral Hyperspectral Image Classification Using Random
           Multiscale Representation
    • Authors: Jianjun Liu;Zebin Wu;Jun Li;Liang Xiao;Antonio Plaza;Jon Atli Benediktsson;
      Pages: 4129 - 4141
      Abstract: This paper presents a novel spatial–spectral classification method for remotely sensed hyperspectral images. First of all, a multiscale representation technique based on random projection, referred as random multiscale representation (RMSR), is proposed to extract the spatial features from the given scene. The idea behind RMSR is to properly model the spatial characteristics comprised by each pixel vector and its neighbors by some criteria computed at all reasonable scales, and then compress the implicit high-dimensional spatial features by using a very sparse measurement matrix that approximately preserves the salient spatial information. The entire process is explicitly performed by computing simple criteria (i.e., the first two moments) at rectangular scales of random bands, according to the nonzero entries of the sparse measurement matrix. Subsequently, a composite kernel framework is utilized to balance the extracted spatial features and the original spectral features in the classifier. Our proposed method is shown to be effective for hyperspectral image classification purposes. Specifically, our experimental results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer and the reflective optics spectrographic imaging system demonstrate the effectiveness of the proposed method as compared to other state-of-the-art spatial–spectral classifiers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Spectral–Spatial Feature Learning Using Cluster-Based Group Sparse
           Coding for Hyperspectral Image Classification
    • Authors: Xiangrong Zhang;Qiang Song;Zeyu Gao;Yaoguo Zheng;Peng Weng;L. C. Jiao;
      Pages: 4142 - 4159
      Abstract: This paper presents a new spectral–spatial feature learning method for hyperspectral image classification, which integrates spectral and spatial information into group sparse coding (GSC) via clusters, each of which is an adaptive spatial partition of pixels. The clusters derived from the segmentation maps by the mean-shift algorithm are regarded as groups in GSC, where pixels within the same group are simultaneously represented by a sparse linear combination of a few common atoms in a given dictionary, thus enforcing spatial smoothness across the pixels in the same segmentation region to learn a spectral–spatial joint sparse representation. Finally, the recovered sparse representation can be viewed as a new feature and used directly for classification (e.g., by support vector machine). In comparison with other spectral–spatial classification techniques that exploit a fixed neighborhood system and force neighboring pixels to share a common sparsity pattern, the proposed method is more flexible and able to obtain adaptive spatial neighborhood correlations for spectral–spatial joint sparse coding. In addition, we also develop kernel GSC (KGSC) by incorporating the kernel trick into GSC to capture nonlinear relationships. The developed KGSC can also be applied to learning kernel sparse representation under the framework of the proposed spectral–spatial method, leading to a new spectral–spatial kernel sparse representation algorithm. Experimental results on three real hyperspectral datasets indicate that the proposed methods improve classification accuracy and provide distinctive classification maps, especially at small regions and boundaries in an image, compared with other similar approaches.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Class-Level Joint Sparse Representation for Multifeature-Based
           Hyperspectral Image Classification
    • Authors: Erlei Zhang;Licheng Jiao;Xiangrong Zhang;Hongying Liu;Shuang Wang;
      Pages: 4160 - 4177
      Abstract: Recent studies show that different features can represent different characteristics of hyperspectral images, and a combination of them would have positive influence on classification. In this paper, we formulate the multifeature hyperspectral image classification as a joint sparse representation model which simultaneously represents the pixels of multiple features (spectral, shape, and texture) with a class-level sparse constraint. The proposed model enforces pixels in a small region of each type features to share the same sparsity pattern; at the same time, the pixels described by different features have freedom to adaptively choose their own appropriate atoms but still belong to the same class. Thus, the proposed model not only preserves the spatial information by joint sparse constraint but also utilizes additional complementary information from different features by class-level sparse constraint. Furthermore, we also kernelize the model to handle nonlinearity in the data. And a new version of simultaneous orthogonal matching pursuit is proposed to solve the aforementioned problems. Experiments on several real hyperspectral images indicate that the proposed algorithms provide a competitive performance when compared with several state-of-the-art algorithms.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Hyperspectral Image Classification by Fusing Collaborative and Sparse
    • Authors: Wei Li;Qian Du;Fan Zhang;Wei Hu;
      Pages: 4178 - 4187
      Abstract: This paper proposes to combine collaborative representation (CR) and sparse representation (SR) for hyperspectral image classification. SR may select too few samples that cannot well reflect within-class variations, while CR generates nonsparse code using all the atoms that may unfortunately include between-class interference. To alleviate these problems, two methods fusing CR and SR are proposed, i.e., a fused representation-based classification (FRC) method and an elastic net representation-based classification (ENRC) method. FRC attempts to achieve the balance between CR and SR in the residual domain, while ENRC uses a convex combination of \ell _1 and \ell _2 penalties. Experimental results on two hyperspectral data demonstrate that the proposed methods outperform the original counterparts, i.e., CR-based classification (CRC) and SR-based classification (SRC).
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Low-Rank Subspace Representation for Supervised and Unsupervised
           Classification of Hyperspectral Imagery
    • Authors: Alex Sumarsono;Qian Du;
      Pages: 4188 - 4195
      Abstract: Although hyperspectral data have very high dimensionality, major information tends to occupy a low-rank subspace and outliers are often found in a sparse matrix. However, due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. In this paper, we propose to use low-rank subspace representation (LRSR) as a preprocessing step for classification in both supervised and unsupervised fashion. In supervised classification, LRSR is shown to improve the performance of various classifiers. In unsupervised classification, both K-means clustering and spectral clustering can be applied on the low-rank matrix to improve the performance. Experimental results demonstrate that the proposed method can increase classification accuracy, particularly for complicated image scenes, and outperform the often-used low-rank representation approach.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Fuzzy Signature-Based Discriminative Subspace Projection for Hyperspectral
           Data Classification
    • Authors: Shuyuan Yang;Hongjing Zhou;Min Wang;Zhixi Feng;Licheng Jiao;
      Pages: 4196 - 4202
      Abstract: Mixed pixels in the hyperspectral image (HSI) are often misclassified under a strict clustering assumption. In this paper, we relax the assumption and assign a fuzzy signature for each pixel in HSI, whose element indicates the probability it belongs to some class. A fuzzy signature-based discriminative subspace projection (FS-DSP) approach is then developed for simultaneous dimensionality reduction and classification of HSI. In FS-DSP, a signature Laplacian regularizer is derived from both labeled and unlabeled pixels to pull the neighbors with similar fuzzy signatures together. A discriminant term is constructed to further pull different classes away and push the same classes toward after the projection. The two terms are combined to define a subspace projection optimization problem, and an alternating direction method of multipliers (ADMM) algorithm is employed to iteratively calculate fuzzy signatures. Effectiveness of FS-DSP is evaluated by five datasets, and the results show that it exhibits state-of-the-art performance as to the numerical guidelines, such as overall accuracy (OA), average accuracy (AA), and Kappa coefficients (KC), when there are only very few labeled pixels.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Spectral-Angle-Based Discriminant Analysis of Hyperspectral Data for
           Robustness to Varying Illumination
    • Authors: Minshan Cui;Saurabh Prasad;
      Pages: 4203 - 4214
      Abstract: In this paper, we assert that a recently developed approach to classify hyperspectral images provides substantial robustness to illumination effects for hyperspectral image analysis. The approach [angular discriminant analysis (ADA) and its variants] seeks to find lower dimensional subspaces, where class-specific information is angularly well-separated and where appropriate classifiers can then be applied. We demonstrate the ADA, and its local and kernel variants provide substantial robustness to data which is affected by such illumination differences (commonly encountered in geospatial images). We validated with two different hyperspectral datasets, both of which contain classes in well-illuminated regions and in shadows. We note that the proposed framework is suitable to the general problem of illumination variation in scenes—we validate with a specifically harsh and commonly encountered scenario where classes in an image are present in regions where they are well illuminated, and regions where they are occluded by shadows. Experimental results show that this dimensionality reduction not only enables robustness to sample size by virtue of feature reduction but also is very effective at capturing class-specific information irrespective of changes in illumination content, in the lower dimensional subspace.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • A New Approach for Endmember Extraction and Clustering Addressing Inter-
           and Intra-Class Variability via Multiscaled-Band Partitioning
    • Authors: Charoula Andreou;Derek Rogge;Rupert Müller;
      Pages: 4215 - 4231
      Abstract: In this paper, a new method is introduced for detecting and clustering spectrally similar but physically distinct materials. The method exploits the spectral information by dividing the spectral domain into band subsets whose width varies from broad to narrower wavelength ranges. Multiple candidate endmembers containing intraclass spectral variability are extracted using a maximum volume-based endmember extraction method at each band subset. Spectral clustering of the extracted spectra is also accomplished by using a multiscaled-band partitioning approach. This allows for the generation of multiscaled clustering identification vectors that can be used to remove partial mixtures and also be used to derive the final set of endmember bundles which retain interclass endmember variability. The proposed method was evaluated using simulated and real hyperspectral data and in comparison with well-known methods for extracting a fixed set or multiple sets of endmembers. Results revealed the advantages of the multiscaled-band partitioning on both multiple endmember extraction and clustering with the latter being an independent module that can be applicable to endmember candidate libraries derived from other methods.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Automated Construction of Multiple Regional Libraries for Neighborhoodwise
           Local Multiple Endmember Unmixing
    • Authors: Chengbin Deng;
      Pages: 4232 - 4246
      Abstract: Endmember variability has been recognized as a major source of error in spectral unmixing. Although numerous unmixing algorithms have been developed, there is a gap between the studies emphasizing automated processing and those focusing on estimation accuracy. Endmember variability is rarely considered in most automated processing approaches, while existing unmixing algorithms that accommodate endmember variability partially rely on human involvement. To fill this gap, an automated unmixing chain prototype is proposed in this research, which integrates endmember extraction, refinement, and multiple endmember spectral mixture analysis (MESMA). In particular, this prototype is designed to automate three processing steps with intelligent approaches, including optimal size determination to generate neighborhoods as a spatial constraint, endmember refinement to select representative endmembers in spectral libraries, and automated construction of multiple regional libraries. Based on this prototype, three specific local and global MESMA variants with different neighborhood types were implemented, and their performances to estimate urban impervious surface abundance were compared. Analyses indicate three major conclusions. First, by examining spatial dependence of endmember spectra, an optimal neighborhood size can be obtained, with which a localized neighborhood can be derived by aggregating spatially close image segments. Second, by applying spatial and iterative K-means spectral clustering to endmembers from the identified neighborhoods, parsimonious and representative endmembers can be refined from a large endmember pool. Accordingly, a local spectral library can be automatically constructed in each neighborhood. Third, neighborhoodwise object-based MESMA (NEW OB-MESMA) significantly outperforms the other two MESMA variants with improved estimation accuracy and increased computational efficiency.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing
    • Authors: Andrea Marinoni;Antonio Plaza;Paolo Gamba;
      Pages: 4247 - 4256
      Abstract: Higher order nonlinear material mixtures provide a good model to explain the effects of physical–chemical phenomena on hyperspectral remote sensing measurements. Therefore, inverting nonlinear effects starting from the measured spectral values is a very challenging yet fundamental task to provide a thorough and reliable characterization of the materials in a scene. In this paper, this task is achieved by inverting a new model for nonlinear hyperspectral mixtures. Specifically, we show that it is possible to effectively unmix hyperspectral data by assuming a harmonic description of the higher order nonlinear combination of the endmembers. The rationale for this model is that the harmonic analysis is able to understand and quantify effects that cannot be effectively described by classic polynomial combinations. Although the model is nonlinear, unmixing is performed by solving a linear system thanks to the recently proposed polytope decomposition (POD). Experimental results show that inverting this model leads to improved performances with respect to the state of the art in terms of endmember abundance estimation both over synthetic and real datasets.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity
           and Total Variation
    • Authors: Hemant Kumar Aggarwal;Angshul Majumdar;
      Pages: 4257 - 4266
      Abstract: Hyperspectral unmixing is the process of estimating constituent endmembers and their fractional abundances present at each pixel in a hyperspectral image. A hyperspectral image is often corrupted by several kinds of noise. This work addresses the hyperspectral unmixing problem in a general scenario that considers the presence of mixed noise. The unmixing model explicitly takes into account both Gaussian noise and sparse noise. The unmixing problem has been formulated to exploit joint-sparsity of abundance maps. A total-variation-based regularization has also been utilized for modeling smoothness of abundance maps. The split-Bregman technique has been utilized to derive an algorithm for solving resulting optimization problem. Detailed experimental results on both synthetic and real hyperspectral images demonstrate the advantages of proposed technique.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Sparsity-Regularized Robust Non-Negative Matrix Factorization for
           Hyperspectral Unmixing
    • Authors: Wei He;Hongyan Zhang;Liangpei Zhang;
      Pages: 4267 - 4279
      Abstract: Hyperspectral unmixing (HU) is one of the crucial steps for many hyperspectral applications, including material classification and recognition. In the last decade, non-negative matrix factorization (NMF) and its extensions have been widely studied and have achieved advanced performances in HU. Unfortunately, most of the existing NMF-based methods make the assumption that the hyperspectral data are only corrupted by Gaussian noise. In real applications, the hyperspectral data are inevitably corrupted by sparse noise, which includes impulse noise, stripes, deadlines, and others types of noise. By separately modeling the sparse noise and Gaussian noise, a robust NMF (RNMF) model is subsequently introduced to unmix the hyperspectral data. The proposed RNMF model is able to simultaneously handle Gaussian noise and sparse noise, and can be efficiently learned with elegant update rules. In addition, sparsity regularizers are added to restrict the abundance maps in the RNMF, with the consideration of the sparse property of the material types within the hyperspectral scene. The experimental results with simulated and real data confirm the superiority of the proposed sparsity-regularized RNMF methods compared to the traditional NMF methods.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding
           Endmembers in Hyperspectral Imagery
    • Authors: Chein-I Chang;Shih-Yu Chen;Hsiao-Chi Li;Hsian-Min Chen;Chia-Hsien Wen;
      Pages: 4280 - 4306
      Abstract: Endmember finding has become increasingly important in hyperspectral data exploitation because endmembers can be used to specify unknown particular spectral classes. Pixel purity index (PPI) and N-finder algorithm (N-FINDR) are probably the two most widely used techniques for this purpose where many currently available endmember finding algorithms are indeed derived from these two algorithms and can be considered as their variants. Among them are three well-known algorithms derived from imposing different abundance constraints, that is, abundance-unconstrained automatic target generation process (ATGP), abundance nonnegativity constrained vertex component analysis (VCA), and fully abundance constrained simplex growing algorithm (SGA). This paper explores relationships among these three algorithms and further shows that theoretically they are essentially the same algorithms in the sense of design rationale. The reason that these three algorithms perform differently is not because they are different algorithms, but rather because they use different preprocessing steps, such as initial conditions and dimensionality reduction transforms.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Evaluation of Temperature and Emissivity Retrieval using Spectral
           Smoothness Method for Low-Emissivity Materials
    • Authors: Yonggang Qian;Ning Wang;Lingling Ma;Chen Mengshuo;Hua Wu;Li Liu;Qijin Han;Caixia Gao;Jia Yuanyuan;Lingli Tang;Chuanrong Li;
      Pages: 4307 - 4315
      Abstract: Land surface temperature and emissivity separation (TES) is a key problem in thermal infrared (TIR) remote sensing. Along with the development of civil applications, increasing numbers of man-made low-emissivity materials can be found around our living environment. In addition, the characteristics and variation in properties of those materials should also be concerned. However, there are still few TES methods for low-emissivity materials reported in the literature. This paper addresses the performance of the automatic retrieval of temperature and emissivity using spectral smoothness (ARTEMISS) method proposed by Borel (2008) for the retrieval of temperature and emissivity from hyperspectral TIR data for low-emissivity materials. The results show that those modeling errors are less than 0.11 K for temperature and 0.3% for emissivity as shown in the ARTEMISS algorithm if atmospheric parameters and the mean emissivity of material spectra are known. A sensitivity analysis has been performed, and the results show that the retrieval accuracy will be degraded with the increase of instrument noises, the errors of the atmospheric parameters, and the coarser spectral resolution. ARTEMISS can give a reasonable estimation of the temperature and emissivity for high- and low-emissivity materials; however, the performance of the algorithm is more seriously influenced by the atmospheric compensation than by the instrument noises. Our results show that the errors of temperature and emissivity become approximately three times than that when the instrument spectral properties are 1{text{ cm}}^{-1} of sampling interval and 2{text{ cm}}^{-1} of FWHM, and 4{text{ cm}}^{-1} of sampling interval and
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes
           and Anomalies in Hyperspectral Images and Movies
    • Authors: Yi Wang;Guangliang Chen;Mauro Maggioni;
      Pages: 4316 - 4324
      Abstract: We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a “mixture of subspaces” model to tackle more complicated background. Furthermore, we use partial least squares regression on a resampled training set to boost performance. For the detection of unknown chemicals, we view the problem as an anomaly detection problem and use novel estimators with low-sampled complexity for intrinsically low-dimensional data in high dimensions that enable us to model the “normal” spectra and detect anomalies. We apply these algorithms to benchmark datasets made available by the Automated Target Detection program cofunded by NSF, DTRA, and NGA, and compare, when applicable, to current state-of-the-art algorithms, with favorable results.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Integrating Hyperspectral Likelihoods in a Multidimensional Assignment
           Algorithm for Aerial Vehicle Tracking
    • Authors: Burak Uzkent;Matthew J. Hoffman;Anthony Vodacek;
      Pages: 4325 - 4333
      Abstract: Tracking vehicles through dense environments is an important and challenging task that is mostly tackled using visible and near IR wavelengths. Hyperspectral imaging is known to improve the robustness of target identification, but the massive increase in data created is usually prohibitive for tracking many targets. We present a persistent real-time aerial target tracking system, taking advantage of an adaptive, multimodal sensor concept and blending the hyperspectral likelihoods with kinematic likelihoods in a multidimensional assignment framework. The adaptive sensor is capable of providing wide field of view panchromatic images as well as the spectra of small number of pixels. The proposed system does not require large amount of hyperspectral data collection as we focus on tracking fewer number of targets with higher persistency. This overcomes the data challenge of hyperspectral tracking by following dynamic data-driven application systems (DDDAS) principles to control hyperspectral data collection where most beneficial. The DDDAS framework for controlling hyperspectral data collection is developed by incorporating prior information from the filter movement predictions and information from motion detection. The proposed multidimensional hyperspectral feature-aided tracker is compared to a 2-D hyperspectral feature-aided tracker and another cascaded hyperspectral data based tracker by generating a synthetic, realistic, aerial video on a dense scene.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • FPGA Implementation of an Algorithm for Automatically Detecting Targets in
           Remotely Sensed Hyperspectral Images
    • Authors: Carlos González;Sergio Bernabé;Daniel Mozos;Antonio Plaza;
      Pages: 4334 - 4343
      Abstract: Timely detection of targets continues to be a relevant challenge for hyperspectral remote sensing capability. The automatic target-generation process using an orthogonal projection operator (ATGP-OSP) has been widely used for this purpose. Hyperspectral target-detection applications require timely responses for swift decisions, which depend upon (near) real-time performance of algorithm analysis. Reconfigurable field-programmable gate arrays (FPGAs) are promising platforms that allow hardware/software codesign and the potential to provide powerful onboard computing capabilities and flexibility at the same time. In this paper, we present an FPGA implementation for the ATGP-OSP algorithm. Our system includes a direct memory access module and implements a prefetching technique to hide the latency of the input/output communications. The proposed method has been implemented on a Virtex-7 XC7VX690T FPGA and tested using real hyperspectral data collected by NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada and the World Trade Center in New York. Experimental results demonstrate that our hardware version of the ATGP-OSP algorithm can significantly outperform a software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • An Investigation Into Machine Learning Regression Techniques for the Leaf
           Rust Disease Detection Using Hyperspectral Measurement
    • Authors: Davoud Ashourloo;Hossein Aghighi;Ali Akbar Matkan;Mohammad Reza Mobasheri;Amir Moeini Rad;
      Pages: 4344 - 4351
      Abstract: The complex impacts of disease stages and disease symptoms on spectral characteristics of the plants lead to limitation in disease severity detection using the spectral vegetation indices (SVIs). Although machine learning techniques have been utilized for vegetation parameters estimation and disease detection, the effects of disease symptoms on their performances have been less considered. Hence, this paper investigated on 1) using partial least square regression (PLSR), \nu support vector regression ( \nu -SVR), and Gaussian process regression (GPR) methods for wheat leaf rust disease detection, 2) evaluating the impact of training sample size on the results, 3) the influence of disease symptoms effects on the predictions performances of the above-mentioned methods, and 4) comparisons between the performances of SVIs and machine learning techniques. In this study, the spectra of the infected and non infected leaves in different disease symptoms were measured using a non imaging spectroradiometer in the electromagnetic region of 350 to 2500 nm. In order to produce a ground truth dataset, we employed photos of a digital camera to compute the disease severity and disease symptoms fractions. Then, different sample sizes of collected datasets were utilized to train each method. PLSR showed coefficient of determination ( R^2 ) values of 0.98 (root mean square error (RMSE) = 0.6) and 0.92 (RMSE = 0.11) at leaf and canopy, respectively. SVR showed R^2 and RMSE close to PLSR at leaf ( R^2 = 0.98, RMSE = 0.05) and canopy ( R^2 = 0.95, RMSE = 0.12- scales. GPR showed R^2 values of 0.98 (RMSE = 0.03) and 0.97 (RMSE = 0.11) at leaf and canopy scale, respectively. Moreover, GPR represents better performances than others using small training sample size. The results represent that the machine learning techniques in contrast to SVIs are not sensitive to different disease symptoms and their results are reliable.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Automatic Band Selection Using Spatial-Structure Information and
           Classifier-Based Clustering
    • Authors: Xianghai Cao;Bin Wu;Dacheng Tao;Licheng Jiao;
      Pages: 4352 - 4360
      Abstract: Band selection plays an important role in hyperspectral image processing, which can reduce subsequent computation and storage requirement. There are two problems that are rarely investigated for band selection. First, some low-discriminating bands need to be manually removed by experts, which is time consuming and expensive; second, how to automatically determine the number of selected bands is not well investigated, though this is an indispensable step in practical applications. In this paper, we propose an automatic band selection (ABS) method to solve these problems. First, we exploit spatial structure to determine the discriminating power of each band, these bands with little structure information will be discarded; then, a powerful classifier is used for clustering, which can automatically find the underlying number of clusters. Experiments based on three real hyperspectral datasets demonstrate the effectiveness of our method.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Spectral–Spatial KerSparseBands Selector
    • Authors: Min Wang;Zhixi Feng;Shuyuan Yang;
      Pages: 4361 - 4373
      Abstract: The existence of mixed pixels in hyperspectral data makes their classification very challenging. In this paper, we propose a new spectral–spatial Kernelized Sparse Bands (SS-KerSparseBands) selector to extract discriminative features for accurate hyperspectral data classification (HDC). In our method, both the intrinsic cube structure of data and the sparse characteristics of features are explored, by formulating spectra as a hyperspectral tensor and reducing feature selection to the spectral–spatial joint sparse coding (SC) of labels. Moreover, a new tensor-multiple measurement vector (TMMV) optimization algorithm is advanced to identify the most significant KerSparseBands for the subsequent Fisher discriminant classification. Some experiments are performed on several synthetic dataset and real Indian Pines, Salinas-A, and Pavia datasets to investigate the performance of SS-KerSparseBands, and the results show that it can accurately classify mixed pixels, and is competitive in terms of classification accuracy and computational complexity when compared to its counterparts.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • A Dissimilarity-Weighted Sparse Self-Representation Method for Band
           Selection in Hyperspectral Imagery Classification
    • Authors: Weiwei Sun;Liangpei Zhang;Lefei Zhang;Yenming Mark Lai;
      Pages: 4374 - 4388
      Abstract: A new dissimilarity-weighted sparse self-representation (DWSSR) method has been presented to select a proper band subset for hyperspectral imagery (HSI) classification. The DWSSR assumes that all the bands can be represented by the selected band subset, and it formulates sparse representation of all the bands into a sparse self-representation (SSR) model with row-sparsity constraint in the coefficient matrix. Furthermore, the DWSSR integrates a dissimilarity-weighted regularization term with the SSR model to avoid the issue of too-close bands encountered in the SSR. The regularization term explains the encoding cost of all bands with the representative bands, and a new composite dissimilarity measure which combines spectral information divergence with intraband correlation is implemented to estimate the encoding weight. The DWSSR program is solved by the alternating direction method of multipliers (ADMM) framework, and the representative bands are finally selected according to the norm rankings of nonzero rows in the estimated coefficient matrix. Five groups of experiments on three popular HSI datasets are designed to test the performance of DWSSR in band selection, and five state-of-the-art methods are utilized to make comparisons. The results show that the DWSSR performs almost best among all the six methods, either in computational time or classification accuracies.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Feature Extraction of Hyperspectral Images With Semisupervised Graph
    • Authors: Renbo Luo;Wenzhi Liao;Xin Huang;Youguo Pi;Wilfried Philips;
      Pages: 4389 - 4399
      Abstract: We propose a semisupervised graph learning (SEGL) method for feature extraction of hyperspectral remote sensing imagery in this paper. The proposed SEGL method aims to build a semisupervised graph that can maximize the class discrimination and preserve the local neighborhood information by combining labeled and unlabeled samples. In our semisupervised graph, we connect labeled samples according to their label information and unlabeled samples by their nearest neighborhood information. By sorting the mean distance between a unlabeled sample and labeled samples of each class, we connect the unlabeled sample with all labeled samples belonging to its nearest neighborhood class. Moreover, the proposed SEGL better models the actual differences and similarities between samples, by setting different weights to the edges of connected samples. Experimental results on four real hyperspectral images (HSIs) demonstrate the advantages of our method compared to some related feature extraction methods.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • GPU-Based Parallel Design of the Hyperspectral Signal Subspace
           Identification by Minimum Error (HySime)
    • Authors: Xin Wu;Bormin Huang;Lizhe Wang;Jianqi Zhang;
      Pages: 4400 - 4406
      Abstract: Signal subspace identification provides a performance improvement in hyperspectral applications, such as target detection, spectral unmixing, and classification. The HySime method is a well-known unsupervised approach for hyperspectral signal subspace identification. It computes the estimated noise and signal correlation matrices from which a subset of eigenvectors is selected to best represent the signal subspace in the least square sense. Depending on the complexity and dimensionality of the hyperspectral scene, the HySime algorithm may be computationally expensive. In this paper, we propose a massively parallel design of the HySime method for acceleration on NVIDIA's graphics processing units (GPUs). Our pure GPU-based implementation includes the optimal use of the page-locked host memory, block size, and the number of registers per thread. The proposed implementation was validated in terms of accuracy and performance using the NASA AVIRIS hyperspectral data. The benchmark with the NVIDIA GeForce GTX 580 and Tesla K20 GPUs shows significant speedups with regards to the optimized CPU-based serial counterpart. This new fast implementation of the HySime method demonstrates good potential for real-time hyperspectral applications.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Semi-realistic Simulations of Natural Hyperspectral Scenes
    • Authors: Zhipeng Hao;Mark Berman;Yi Guo;Glenn Stone;Iain Johnstone;
      Pages: 4407 - 4419
      Abstract: Many papers in the hyperspectral literature use simulations (based on a linear mixture model) to test algorithms, which either estimate the “intrinsic” dimensionality (ID) of the data or endmembers. Usually, these simulations use “real-world” endmembers, proportions distributed according to a uniform or Dirichlet distribution on the endmember simplex, and Gaussian errors which are “spectrally” and “spatially” uncorrelated. When the error standard deviations (SDs) in different bands are assumed to be unequal, they are usually estimated using Roger's method. The simulated and real-world data in these papers are so different that one cannot be confident that the various advocated methods work well with real-world data. We propose a general methodology which produces more realistic simulations, providing us with greater insights into the strengths and weaknesses of various advocated methods. With the aid of the well-known Indian Pines and Cuprite scenes, we compare several specific options within the proposed methodological framework. We also compare the performance of five well-known ID estimators using both real and simulated datasets and demonstrate that Roger's SD estimates are positively biased. A proof that Roger's estimates are always positively biased is given.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Denoising of Hyperspectral Images Using Group Low-Rank Representation
    • Authors: Mengdi Wang;Jing Yu;Jing-Hao Xue;Weidong Sun;
      Pages: 4420 - 4427
      Abstract: Hyperspectral images (HSIs) have been used in a wide range of fields, such as agriculture, food safety, mineralogy, and environment monitoring, but being corrupted by various kinds of noise limits its efficacy. Low-rank representation (LRR) has proved its effectiveness in the denoising of HSIs. However, it just employs local information for denoising, which results in ineffectiveness when local noise is heavy. In this paper, we propose an approach of group low-rank representation (GLRR) for the HSI denoising. In our GLRR, a corrupted HSI is divided into overlapping patches, the similar patches are combined into a group, and the group is reconstructed as a whole using LRR. The proposed method enables the exploitation of both the local similarity within a patch and the nonlocal similarity across the patches in a group simultaneously. The additional nonlocally similar patches can bring in extra structural information to the corrupted patches, facilitating the detection of noise as outliers. LRR is applied to the group of patches, as the uncorrupted patches enjoy intrinsic low-rank structure. The effectiveness of the proposed GLRR method is demonstrated qualitatively and quantitatively by using both simulated and real-world data in experiments.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Multidimensional Striping Noise Compensation in Hyperspectral Imaging:
           Exploiting Hypercubes’ Spatial, Spectral, and Temporal Redundancy
    • Authors: Pablo Meza;Jorge E. Pezoa;Sergio N. Torres;
      Pages: 4428 - 4441
      Abstract: In this paper, two novel multidimensional striping noise compensation (SNC) algorithms for push-broom hyperspectral cameras (PBHCs) have been developed. The SNC algorithms employ a novel pixelwise, affine image-degradation model, which assumes that the striping noise (SN) parameters are spatially uncorrelated, spectrally independent, and decoupled from the camera’s spectral response. Algorithms simultaneously exploit the spatial and temporal information contained in an image as well as the spectral information contained at adjacent spectral images. The multidimensional SNC algorithms were successfully tested on real hyperspectral data from both a commercial PBHC operating in the spectral range of 400–1000 nm, at a resolution of 1.04 nm, and the ESA earth-observing CHRIS/PROBA sensor.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • A Spectrally Weighted Structure Tensor for Hyperspectral Imagery
    • Authors: Maider J. Marin-McGee;Miguel Velez-Reyes;
      Pages: 4442 - 4449
      Abstract: The structure tensor (ST) for vector valued images such as hyperspectral images (HSIs) is most often defined as the average of the scalar STs in each band. The problem with this definition for HSI is the assumption that all bands provide the same amount of edge information giving them the same weights. As a result, nonedge pixels can be reinforced and edges can be weakened resulting in a poor performance by algorithms that depend on the ST. In this paper, a spectrally weighted ST for HSI is proposed. The weights are motivated by the fact that in HSI, neighboring spectral bands are highly correlated, as are the bands of its gradient. The proposed scheme gives higher weight where significant changes in the gradient between bands are detected. The spectrally weighted ST is used in tensor nonlinear anisotropic diffusion (TAND) for edge enhancing diffusion (EED). Comparisons with Weicker’s uniform weighting show that the spectrally weighted ST better discriminates edges with EED. Experimental results using the airborne visible/infrared imaging spectrometer (AVIRIS) Indian Pines and Cuprite HSIs are presented.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Correcting Bidirectional Effect for Multiple-Flightline Aerial Images
           Using a Semiempirical Kernel-Based Model
    • Authors: Zhihui Wang;Liangyun Liu;
      Pages: 4450 - 4463
      Abstract: Due to the intrinsic observing characteristics of airborne sensors, the bidirectional effect is inevitable and happens regardless of the number of flightlines being considered. This affects the quantitative use of aerial data over large regions. In this paper, a simple “two-step” bidirectional effect correction scheme based on Ross–Li model (RLM) is developed for multiple-flightline aerial images. First, the local RLM coefficients and local correction factors (K1) for each flightline were derived independently based on original observed reflectance; next, the global RLM coefficients and global correction factors (K 2) for all flightlines were derived based on simulated directional-to-nadir reflectance. Nadir view bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR) from multiple-flightline for fixed illumination condition of “base flightline” was produced using the combination of the two correction factors (K 1, K2). Correction experiments conducted using multiple-flightline push-broom compact airborne spectrographic imager aerial images and the NBAR produced by the “two-step” BRDF correction method were compared to other “one-step” methods. The results show that the “two-step” method gave a better BRDF correction performance—a slower trend change for average of NBARs at a constant viewing angle as varying sun–target–sensor geometry, indicating that the trends of bidirectional effects within a given flightline and between flightlines were effectively normalized. It is concluded that our normalization scheme can be applied to remove bidirectional effects-of multiple-flightline aerial images without multiangular observations if reasonable land-cover types in the aerial images were determined.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Poissonian Hyperspectral Image Superresolution Using Alternating Direction
    • Authors: Changzhong Zou;Youshen Xia;
      Pages: 4464 - 4479
      Abstract: The reconstruction of Poissonian image is an active research area in recent years. This paper proposes a novel method for Poissonian hyperspectral image superresolution by fusing a low-spatial-resolution hyperspectral image and a low-spectral-resolution multispectral image. The fusion scheme is designed as an optimization problem, whose cost function consists of the two data-fidelity terms about Poisson distribution, the sparse representation term, and the nonlocal regularization term. The two data-fidelity terms can capture statistical information of Poisson noise. The sparse representation term is used for enhancing the quality of sparsity-based signal reconstruction, and the nonlocal regularization term exploits the spatial similarity of hyperspectral image. As a result, the hyperspectral image and multispectral image are well fused. Finally, the designed optimization problem is effectively solved by an alternating direction optimization algorithm. Simulation results illustrate that the proposed method has a better performance than several well-known methods both in terms of quality indexes and reconstruction visual effect.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • A New Genetic Method for Subpixel Mapping Using Hyperspectral Images
    • Authors: Xiaohua Tong;Xiong Xu;Antonio Plaza;Huan Xie;Haiyan Pan;Wen Cao;Dong Lv;
      Pages: 4480 - 4491
      Abstract: Subpixel mapping techniques aim to obtain the spatial location and distribution of subpixels by transforming the information coming from a set of input abundance maps into a classification result with higher spatial resolution. However, traditional subpixel mapping algorithms generally ignore the possible errors that are due to abundance estimation inaccuracies by spectral unmixing techniques. In this paper, we propose a new genetic algorithm-based subpixel mapping technique that solves the subpixel mapping problem by correcting the potential errors in the estimated abundance fractions used as input to the subpixel mapping process. The proposed algorithm has been compared with other two genetic subpixel mapping methods, using both synthetic and real hyperspectral images. Our experimental results demonstrate that the proposed approach outperforms traditional subpixel mapping algorithms, thus providing an effective option to improve the accuracy of subpixel mapping for remotely sensed hyperspectral images.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Hybrid Norm Pursuit Method for Hyperspectral Image Reconstruction
    • Authors: Jihao Yin;Wanke Yu;Xiuping Jia;
      Pages: 4492 - 4500
      Abstract: This paper proposes a hybrid l_{0} and l_{1} norm pursuit (HNP) method for reconstructing hyperspectral image with high speed and high fidelity. The HNP method provides an approximate result by a simple and fast l_{0} norm algorithm [such as the orthogonal matching pursuit (OMP)] first and then regulates it to an accurate result by a good but slow l_{1} norm algorithm [such as the gradient projection for sparse reconstruction (GPSR)]. We build a mathematic model for the HNP method and formulate it to be a constraint optimization problem. How to choose the best switch point is investigated to ensure that the HNP method is able to provide the best reconstruction performance. Experimental results demonstrate that the HNP method is fast and offers high accuracy for hyperspectral image reconstruction and classification.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Adaptive Sampling by Dictionary Learning for Hyperspectral Imaging
    • Authors: Mingrui Yang;Frank de Hoog;Yuqi Fan;Wen Hu;
      Pages: 4501 - 4509
      Abstract: In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary from training spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain the compressive measurements for reconstruction. The proposed method provides significant improvement over the conventional compressive sensing approaches. The reconstruction performance is further improved by reconditioning the sensing matrix using matrix balancing. We also demonstrate that the combination of dictionary learning and SVD is robust by applying them to different datasets.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Special Issue on Advances in Agro-geoinformatics Research and Application
    • Pages: 4510 - 4510
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Special Issue on Ground Penetrating Radar for Remote Sensing Applications
    • Pages: 4511 - 4511
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Special Issue on Contributions to Global Water Cycle Science and
           Applications from GCOM-W/AMSR2
    • Pages: 4512 - 4512
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Special Issue on Remote Sensing Data Simulation
    • Pages: 4513 - 4513
      Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Introducing IEEE collabratec
    • Pages: 4514 - 4514
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Become a published author in 4 to 6 weeks
    • Pages: 4515 - 4515
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
  • Expand Your Network, Get Rewarded
    • Pages: 4516 - 4516
      Abstract: Advertisement, IEEE.
      PubDate: Sept. 2016
      Issue No: Vol. 9, No. 9 (2016)
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