Abstract
This paper presents a subpixel spatial-spectral feature mining approach for hyperspectral image classification. First, a regional clustering-based spatial preprocessing (RCSPP) strategy is introduced to identify the endmember signatures from the original image. Then, a partial unmixing model of mixture tuned matched filtering (MTMF) is adopted to estimate the abundance maps. Finally, the morphological component analysis (MCA) is adopted to decompose the abundance map into different spatial morphological components, and the smoothness components are chosen for classification. The experimental results reveal that the obtained subpixel spatial-spectral feature can lead to very good classification accura-
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