Spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection

• DOI: 10.1016/j.infrared.2018.06.001
• Publication Date: 2018-06-01T12:56:34Z
• Authors: Chunhui Zhao; Lili Zhang
• Keywords: 0211 other engineering and technologies; 02 engineering and technology

Abstract Nowadays, some algorithms based on deep learning have drawn increasing attention in hyperspectral image (HSI) analysis. In this paper, we propose spectral-spatial stacked autoencoders based on low-rank and sparse matrix decomposition (LRaSMD-SSSAE) for hyperspectral anomaly detection (AD). First, the Go Decomposition (GoDec) algorithm is employed to solve the low-rank background component and the sparse anomaly component. Second, stacked autoencoders (SAE) are employed on the sparse matrix for spectral deep features and on the low-rank matrix for spatial deep features, respectively. Finally, the spectral-spatial feature matrix is established and local Mahalanobis-distance algorithm is employed for the final detection result. Experiments are carried out on real and synthetic HSI, and the results show that the proposed LRaSMD-SSSAE generally outperforms the comparison algorithms.