A sample set perspective on the classification of hyperspectral image with weighted affine constraint

Hyperspectral images (HSIs) provide abundant spectral information of land covers, allowing detailed analysis of the materials on the earth. Besides, the homogeneity of land covers' distribution, that neighboring pixels usually belong to the same class, is also an important spatial information in HSI analysis. In this paper, a novel classification method in a sample set perspective is proposed to jointly exploit the spectral and the spatial information. More specifically, the proposed method treats the neighboring pixels as a sample set from the same class, and then classifies the set in the affine subspace spanned by the samples of the set using sparse representation based classification. In order to reduce the impact of possible outliers in the set, a weighted affine constraint is enforced on the combination coefficients. With this remedy, the proposed method is not only effective in exploiting the complementary information among the neighboring pixels of the same class, but also robust to possible outliers caused by neighbors of other classes. Experiments on two popular benchmarks demonstrate that our algorithm outperforms several state-of-the-art approaches for HSI classification with limited training samples.