1 May 2000 Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery
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Subpixel detection in multispectral imagery presents a challenging problem due to relatively low spatial and spectral resolution. We present a generalized constrained energy minimization (GCEM) approach to detecting targets in multispectral imagery at subpixel level. GCEM is a hybrid technique that combines a constrained energy minimization (CEM) method developed for hyperspectral image classification with a dimensionality expansion (DE) approach resulting from a generalized orthogonal subspace projection (GOSP) developed for multispectral image classification. DE enables us to generate additional bands from original multispectral images nonlinearly so that CEM can be used for subpixel detection to extract targets embedded in multispectral images. CEM has been successfully applied to hyperspectral target detection and image classification. Its applicability to multispectral imagery is yet to be 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 by expanding the original data dimensionality. Experiments show that the proposed GCEM detects targets more effectively than GOSP and CEM without dimensionality expansion.
Chein-I Chang, Chein-I Chang, JihMing Liu, JihMing Liu, BinChang Chieu, BinChang Chieu, Hsuan Ren, Hsuan Ren, Chuin-Mu Wang, Chuin-Mu Wang, ChienShun Lo, ChienShun Lo, Pau-Choo Chung, Pau-Choo Chung, Ching-Wen Yang, Ching-Wen Yang, DyeJyun Ma, DyeJyun Ma, } "Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery," Optical Engineering 39(5), (1 May 2000). https://doi.org/10.1117/1.602486 . Submission:

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