A generalized constrained energy minimization approach to subpixel detection for multispectral imagery

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.