A linear mixture analysis-based compression for hyperspectral image analysis

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In this paper, the authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it ... continued below

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Chang, C. I. & Ginsberg, I. W. June 30, 2000.

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In this paper, the authors present a fully constrained least squares linear spectral mixture analysis-based compression technique for hyperspectral image analysis, particularly, target detection and classification. Unlike most compression techniques that directly deal with image gray levels, the proposed compression approach generates the abundance fractional images of potential targets present in an image scene and then encodes these fractional images so as to achieve data compression. Since the vital information used for image analysis is generally preserved and retained in the abundance fractional images, the loss of information may have very little impact on image analysis. In some occasions, it even improves analysis performance. Airborne visible infrared imaging spectrometer (AVIRIS) data experiments demonstrate that it can effectively detect and classify targets while achieving very high compression ratios.

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OSTI as DE00758103

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  • Conference title not supplied, University of Maryland, College Park, MD (US), No date supplied

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  • Report No.: DOE/NV/11718--434
  • Grant Number: AC08-96NV11718
  • Office of Scientific & Technical Information Report Number: 758103
  • Archival Resource Key: ark:/67531/metadc707373

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  • June 30, 2000

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  • Sept. 12, 2015, 6:31 a.m.

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  • Feb. 11, 2016, 9:21 p.m.

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Chang, C. I. & Ginsberg, I. W. A linear mixture analysis-based compression for hyperspectral image analysis, article, June 30, 2000; Nevada. (digital.library.unt.edu/ark:/67531/metadc707373/: accessed April 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.