Adaptive Dual-Domain Learning for Hyperspectral Anomaly Detection With State-Space Models

Recently, learning-based hyperspectral anomaly detection (HAD) methods have demonstrated outstanding performance, dominating mainstream research. However, the existing learning-based approaches still have two issues: 1) they rarely consider both the spatial sparsity and the interspectral similarity of hyperspectral imagery (HSI) simultaneously and 2) they treat all regions equally, often overlooking the importance of high-frequency information in HSI, which is key to distinguish background and anomalies. To address these challenges, we propose a novel HAD method based on spatial-spectral adaptive dual-domain learning, termed SSHAD. Specifically, we first introduce the spatial-wise selected state space module (SSSM) with linear complexity and the spectral-wise frequency division self-attention module (FDSM), which are combined in parallel to form a spatial-spectral block (SS-block). The SSSM captures the global receptive field by scanning the HSI spatial dimension through a multidirectional scanning mechanism. The FDSM extracts high-frequency and low-frequency information from the HSI via the discrete wavelet transform (DWT) and applies multiscale convolution and self-similarity attention respectively, ensuring the suppression of anomalies during the reconstruction process. This parallel structure enables the network to model cross-window connections, expanding its receptive field while maintaining linear complexity. We use the SS-block as the main component of our adaptive dual-domain learning network, forming SSHAD. Furthermore, we introduce a frequency-wise loss function to inhibit the reconstruction of high-frequency anomalies during background reconstruction. Comprehensive experiments conducted on four public datasets and two unmanned aerial vehicle (UAV)-borne datasets validate the superiority and effectiveness of SSHAD. The code will be publicly available at https://github.com/CZhu0066/SSHAD.