Low-Rank Subspace Representation for Supervised and Unsupervised Classification of Hyperspectral Imagery

Although hyperspectral data have very high dimensionality, major information tends to occupy a low-rank subspace and outliers are often found in a sparse matrix. However, due to the mixed nature of hyperspectral data, the underlying data structure may include multiple subspaces instead of a single subspace. In this paper, we propose to use low-rank subspace representation (LRSR) as a preprocessing step for classification in both supervised and unsupervised fashion. In supervised classification, LRSR is shown to improve the performance of various classifiers. In unsupervised classification, both K-means clustering and spectral clustering can be applied on the low-rank matrix to improve the performance. Experimental results demonstrate that the proposed method can increase classification accuracy, particularly for complicated image scenes, and outperform the often-used low-rank representation approach.