Collaborative Representation-Based Attention Network for Hyperspectral Anomaly Detection

Abstract

The collaborative representation-based detector (CRD) performs anomaly detection for hyperspectral (HS) data using a linear representation of local neighbors for background estimation, which may not fully capture the informational content and spectral variability in complex HS images with heterogenous background. To deal with this aspect, the collaborative representation-based attention network (CRAN) is introduced in this letter, providing a nonlinear representation of data samples for background estimation. Both local neighbors and global samples are used in parallel, and their outputs are fused through a cross-attention mechanism. Experimental results show a good performance of CRAN in comparison with several state-of-the-art anomaly detectors.