Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation for Hyperspectral Images

Abstract

Hyperspectral images (HSIs) own inherent complexity, so, clustering for HSIs is a very challenging task. In this paper, we utilized a semi-supervised subspace clustering method based on non-negative low-rank representation (NNLRR) algorithm for HSI clustering. Firstly, NNLRR used Gaussian fields and harmonic functions into the low-rank representation (LRR) model. Secondly, NNLRR guided the affinity matrix construction by the supervision information. Next, finding a non-negative low-rank matrix, the matrix represents each sample by some other linear combination points, and the affinity matrix is obtained by the matrix. Then, accomplishing the affinity matrix construction and subspace clustering simultaneously. Thanks for the unification of the two steps, we can guarantee the overall optimum. Experimental results on classical data set show that, the algorithm is effective for hyperspectral image clustering.