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Proceedings Paper

Unsupervised target subpixel detection in hyperspectral imagery
Author(s): Chein-I Chang; Qian Du; Shao-Shan Chiang; Daniel C. Heinz; Irving W. Ginsberg
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Paper Abstract

Most subpixel detection approaches require either full or partial prior target knowledge. In many practical applications, such prior knowledge is generally very difficult to obtain, if not impossible. One way to remedy this situation is to obtain target information directly from the image data in an unsupervised manner. In this paper, unsupervised target subpixel detection is considered. Three unsupervised learning algorithms are proposed, which are the unsupervised vector quantization (UVQ) algorithm, unsupervised target generation process (UTGP) and unsupervised NCLS (UNCLS) algorithm. These algorithms produce necessary target information from the image data with no prior information required. Such generated target information is referred to as a posteriori target information and can be used to perform target detection.

Paper Details

Date Published: 20 August 2001
PDF: 10 pages
Proc. SPIE 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII, (20 August 2001); doi: 10.1117/12.437027
Show Author Affiliations
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Qian Du, Texas A&M Univ./Kingsville (United States)
Shao-Shan Chiang, Univ. of Maryland/Baltimore County (Taiwan)
Daniel C. Heinz, Univ. of Maryland/Baltimore County (United States)
Irving W. Ginsberg, U.S. Dept. of Energy (United States)

Published in SPIE Proceedings Vol. 4381:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII
Sylvia S. Shen; Michael R. Descour, Editor(s)

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