Hyper-Laplacian Regularized Low-Rank Tensor Decomposition for Hyperspectral Anomaly Detection

Xiaoxiao Ma, Xiangrong Zhang, Ning Huyan, Xu Tang, Biao Hou, Licheng Jiao

Abstract: This paper presents a novel method for hyperspectral anomaly detection considering the spectral redundancy and exploiting spectral-spatial information at the same time. We proposed a Hyper-Laplacian regularized low-rank tensor decomposition method combing with dimensionality reduction framework. Firstly, k-means++ algorithm is implemented to spectral bands and centers of each group are selected to reduce the HSI dimensionality in spectral direction. To jointly utilize spectral-spatial information, the cubic data (two spatial dimensions and one spectral dimension) is treated as a 3-order tensor. Then the non-local self-similarity is fully explored in our method. For the reason to reduce the ringing artifacts caused by over-lapped segmentation in exploring the non-local self-similarity, we introduce the hyper-Laplacian constrained low-rank tensor decomposition and we get the separated background and residual parts. Finally, to eliminate the effect of Gaussian noise, we use local-Rx basic detector to detect the residual matrix. Experimental results on two real hyperspectral data sets verified the effectiveness of the proposed algorithms for HSI anomaly detection.