Hyperspectral Anomaly Detection via Enhanced Low-Rank and Smoothness Fusion Regularization Plus Saliency Prior

In recent years, tensor representation-based approaches have been widely studied in hyperspectral anomaly detection. However, these methods still suffer from two key issues. First, the various complex regularizations imposed on the background components increase the cost of selecting the best regularized parameters and fail to maximize the effectiveness between these prior regularizations. Second, most of them tend to utilize multiple prior knowledge to describe background components, but show obvious deficiencies in mining prior information of abnormal components. To address these two problems simultaneously, we propose an enhanced low-rank and smoothness fusion regularization plus saliency prior (ELRSF-SP) approach. To be specific, for the first problem, we design a weighted tensor-correlated total variation (wt-CTV) to simultaneously characterize the low-rank and smoothness properties of the background tensor. The wt-CTV avoids an additional regularization parameter to balance the two prior regularizations and fully considers the prior distribution information of the singular values of the gradient tensor, thereby improving the ability and flexibility to cope with practical problems. For the second problem, we construct a saliency weight tensor as a constraint of the anomaly tensor to improve the contrast between abnormal pixels and the background. Meanwhile, the tensor $\ell _{1}$-norm is introduced in ELRSF-SP to characterize the sparsity of the anomaly tensor. Finally, for the optimization of ELRSF-SP, an effective algorithm based on the alternating direction method of multipliers is derived. Extensive experiments demonstrate the effectiveness of the ELRSF-SP approach.