Prototype-Guided Spatial–Spectral Interaction Network for Hyperspectral Anomaly Detection

In recent years, deep learning has emerged as one of the most widely utilized techniques in hyperspectral anomaly detection (HAD) with an impressive detection accuracy. However, the investigation into the diverse background representation and the spatial-spectral interaction remains underexplored. To tackle with this, we propose a novel framework namely the prototype-guided and spatial-spectral interaction network (PSSIN) for HAD in this paper. Specifically, an adaptive anomaly mask module is utilized to mitigate the interference of the background reconstruction caused by the blending of potential anomalies. Subsequently, we design a background-guided prototype autoencoder (BP-AE) to represent the backgrounds with various land cover types, incorporating two critical components: the background prototype module (BPM) and the spatial spectral interaction block (SSIB). To characterize different typical background features by a global perspective, BPM utilizes a prototype learning strategy with a self-attention mechanism, and a multivariate ensemble loss is employed for BPM to optimize the transformation of background features and the updating of a prototype codebook. To enhance the spatial-spectral utilization of window-based approach, SSIB first introduce a spatial-spectral interaction paradigm for HAD. The window-based self-attention branch is to mine spatial features characteristics, while the depth-wise convolution branch is to extract spectral features. These two branches in a parallel configuration interact with each other's features and then perform feature fusion. SSIB architecture not only broadens the receptive fields by concurrently modeling the intra-window and cross-window relationships but also facilitates bi-directional interactions between the spatial and spectral branches. Furthermore, the comprehensive experiments conducted on six authentic datasets have fully validated its superior performance.