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Title: 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.
Authors:
;
Publication Date:
OSTI Identifier:
758103
Report Number(s):
DOE/NV/11718--434
TRN: AH200021%%322
DOE Contract Number:
AC08-96NV11718
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, University of Maryland, College Park, MD (US), No date supplied; Other Information: PBD: 30 Jun 2000
Research Org:
Bechtel Nevada Corp. (US)
Sponsoring Org:
US Department of Energy (US)
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; DATA ANALYSIS; IMAGE PROCESSING; LEAST SQUARE FIT; CLASSIFICATION; IMAGES; SPECTROMETERS