Hyperspectral image classification with low-rank subspace and sparse representation

Hyperspectral image classification based on low-rank representation is considered. It is often assumed that major signals occupy a low-rank subspace, and the remaining component is sparse. Due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. Therefore, in this paper, we propose to use low-rank subspace representation for classification. It can improve the performance of various classifiers, including the traditional linear discriminant analysis followed by maximum likelihood classifier. The performance of using low-rank subspace representation is much better than that of low-rank representation.