Generalized constrained energy minimization approach to subpixel target detection for multispectral imagery

Chein I. Chang, Jih Ming Liu, Bin Chang Chieu, Hsuan Ren, Chuin Mu Wang, Chien Shun Lo, Pau Choo Chung, Ching Wen Yang, Dye Jyun Ma

Research output: Contribution to journalArticlepeer-review

82 Scopus citations


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.

Original languageEnglish
Pages (from-to)1275-1281
Number of pages7
JournalOptical Engineering
Issue number5
StatePublished - 2000


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