Go-Decomposition-Based Iterative Compressed Sampling for Hyperspectral Anomaly Detection

The low-rank and sparse matrix decomposition (LSMD) approach effectively separates background components from anomalies in hyperspectral anomaly detection (HAD), suppressing background interference and enhancing target saliency. However, the high dimensionality, spectral redundancy, and massive data volume pose significant challenges for processing hyperspectral images (HSI). To address these issues, selecting efficient dimensionality reduction strategies for concise and effective extraction of background and anomaly information is crucial for robust anomaly detection. In this paper, we use compressed sensing technology to reduce the dimensionality of HSI data and establish a go decomposition-based iterative multiple random compressed sampling anomaly detection method (IMRCSGD). The IMRCSGD applies the low-rank and sparse matrix decomposition in compressed sampling band domains, and performs RX and RAD anomaly detection in a compressed composite subspace. Specifically, IMRCSGD employs multiple compressive sampling processes to preserve as much significant spectral-spatial information as possible. In addition, a novel iteration mechanism is introduced to reduce the random characteristic and improve the detection performance by incorporating feedback from the texture and content of the newly generated detection results. The effectiveness of the IMRCSGD is validated by experimental results obtained from six real hyperspectral datasets.