Anomaly Detection in Hyperspectral Images via Regularization by Denoising

In a recent work, Fu et al. (2021) proposed an anomaly detector (AD) for hyperspectral images called DeCNN-AD, which decomposes the image in a low rank representation of the background and the anomalies. DeCNN-AD is regularized by an implicit plug and play prior (PnP), providing state-of-the-art anomaly detection performance in hyperspectral images. In this article, we propose a different AD using the Regularization by Denoising (RED) framework. The regularizer that emerges in RED algorithms is advantageous in the sense that it can be minimized by many solvers, unlike PnP, which has to be rederived for each specific solver like the alternating direction method of multipliers or the proximal gradient method. The proposed detector was compared to DeCNN-AD in several experiments, and was shown to present a more stable behavior better convergence properties, to be less sensitive to the choice of parameters, while leading to very similar performance in terms of optimal AUC of receiver operating characteristic curves.