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14 December 1999 Generalized constrained energy minimization approach to subpixel detection for multispectral imagery
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
JihMing Liu, ChunMu Wang, BinChang Chieu, Chein-I Chang, Hsuan Ren, and Ching-Wen Yang "Generalized constrained energy minimization approach to subpixel detection for multispectral imagery", Proc. SPIE 3871, Image and Signal Processing for Remote Sensing V, (14 December 1999);

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