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Proceedings Paper

Generalized constrained energy minimization approach to subpixel detection for multispectral imagery
Author(s): JihMing Liu; ChunMu Wang; BinChang Chieu; Chein-I Chang; Hsuan Ren; Ching-Wen Yang
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Paper Abstract

Subpixel detection for multispectral imagery presents a challenging problem due to relatively low spectral resolution. This paper proposes a Generalized Constrained Energy Minimization (GCEM) approach to detecting objects in multispectral imagery at subpixel level. GCEM is a combination of a dimensionality expansion (DE) approach resulting from a generalized orthogonal subspace projection (GOSP) developed for multispectral image classification and a CEM method developed for hyperspectral image classification. DE allows us to generate additional bands from original multispectral images while CEM is used for subpixel detection to extract objects embedded in multispectral images. CEM has been successfully applied to hyperspectral target detection and image classification. Its applicability to multispectral imagery has not been investigated. A potential limitation of CEM on multispectral imagery is the effectiveness of interference elimination due to the lack of sufficient dimensionality. DE is introduced to mitigate this problem. Experiments have shown that the proposed GCEM detects objects more effectively than CEM without dimensionality expansion and GOSP.

Paper Details

Date Published: 14 December 1999
PDF: 11 pages
Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999); doi: 10.1117/12.373250
Show Author Affiliations
JihMing Liu, National Taiwan Science and Technology Univ. (Taiwan)
ChunMu Wang, National Cinyi Institute of Technology (Taiwan)
BinChang Chieu, National Taiwan Science and Technology Univ. (Taiwan)
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Hsuan Ren, Univ. of Maryland/Baltimore County (United States)
Ching-Wen Yang, Taichung Veterans General Hospital (Taiwan)

Published in SPIE Proceedings Vol. 3871:
Image and Signal Processing for Remote Sensing V
Sebastiano Bruno Serpico, Editor(s)

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