Hyperspectral Compressed Reconstruction for Anomaly Detection Based on Structured Spectral Dual-Tensor Coupled Decomposition

Hyperspectral imaging provides critical information for physical target characterization and anomaly sensing through high-dimensional spectral measurements. However, in practical imaging systems, constrained by sensor bandwidth, storage capacity, and energy consumption, hyperspectral data are often acquired in a compressed measurement. This makes it difficult to obtain accurate and reliable anomaly detection results from such incomplete measurements. Existing methods typically follow a sequential framework, in which compressed sensing reconstruction (CSR) is performed first, followed by hyperspectral anomaly detection (HAD). The two processes hinder their potential for synergistic optimization. To this end, we propose an integrated model called structured spectral dual-tensor coupled decomposition (SSDTCD), which achieves synchronized optimization of CSR and HAD tasks from compressed measurements. The model enhances the joint representational capability among spectral bands through a structured coupled spectral low-rank decomposition (SCSLRD), thereby improving the robustness of sparse reconstruction and the accuracy of HAD. Furthermore, a dual-tensor sparse-enhanced decomposition (DTSED) is incorporated to achieve coupled optimization of low-rank structures and sparse components. Experiments on different datasets show that SSDTCD outperforms several state-of-the-art methods in both reconstruction accuracy and HAD precision. Moreover, it maintains stable and reliable sensing capability even under extremely low sampling rates, validating its effectiveness and practical value under constrained measurement conditions.