MANIFOLD REGULARIZED LOW-RANK REPRESENTATION FOR HYPERSPECTRAL ANOMALY DETECTION

A novel method for hyperspectral anomaly detection based on low-rank representation with manifold regularization is proposed in this paper. Usually, a hyperspectral imagery can be modeled as a superposition of two parts: background part with low rank dimensionality and anomaly part described by a sparse matrix. Low-rank representation (LRR) can be used to find the lowest rank representation of all pixels jointly which represents the background part, then the anomaly part is contained in the residual of the original image. To learn a more discriminative representation, we incorporate the manifold regularization term into the original LRR model. An important advantage of the proposed method is that it can utilize the global low rank property and local geometrical structure jointly. The experimental results on both simulated and real hyperspectral datasets validate the effectiveness of the proposed method.