A linear mixture analysis-based compression for hyperspectral image analysis
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.
- Research Organization:
- Bechtel Nevada Corp. (US)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- AC08-96NV11718
- OSTI ID:
- 758103
- Report Number(s):
- DOE/NV/11718-434; TRN: AH200021%%322
- Resource Relation:
- Conference: Conference title not supplied, University of Maryland, College Park, MD (US), No date supplied; Other Information: PBD: 30 Jun 2000
- Country of Publication:
- United States
- Language:
- English
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