Deep Self-Representation Learning Framework for Hyperspectral Anomaly Detection

Recently, the autoencoder (AE)-based methods in hyperspectral anomaly detection (HAD) have attracted a lot of attention from scholars and researchers, and they acquire satisfying detection accuracy. However, most existing AE-based methods ignore good prior knowledge, which is the low rank and sparse property in a hyperspectral image (HSI). To this end, a novel deep self-representation learning framework (DSLF) is presented in this article. The DSLF combines the robust principal component analysis (RPCA) with the AE model to achieve an alternating optimization (AO), and it is adaptive to remove anomalies and optimize the background reconstruction. To further characterize the HSI background, a novel subspace recovery AE (SRAE) is put forward and a joint loss function is introduced to construct the background subspace, which makes anomalies farther away from the background subspace. Notably, the SRAE is different from the previous AE-based model. The superpixel segmentation divides an HSI into some locally homogeneous regions, and the training and testing of each SRAE is based on one of the local regions. To fully exploit the spatial information, the multiscale strategy is proposed to generate several reconstruction results, and these results are fused to further optimize detection performance. Additionally, the experimental results on the six HSI datasets verify that the proposed DSLF is superior to the state-of-the-art methods.