AUC value comparison.

This paper presents a novel hyperspectral anomaly detection (HAD) method, ETRPCA-CRD, which integrates enhanced tensor robust principal component analysis (ETRPCA) with collaborative representation detection (CRD) to effectively separate anomalous targets from background data. The key novelty lies in the use of weighted tensor Schatten-p norm minimization (WTSNM) within the ETRPCA framework, which assigns distinct weights to different singular values to preserve important information while eliminating noise. The ETRPCA problem is efficiently solved by Fourier transform, generalized soft-thresholding (GST), and T-singular value decomposition (SVD) methods. This approach significantly improves detection accuracy by fully utilizing the spectral-spatial information of hyperspectral images (HSIs) represented as tensors. The low-rank tensor obtained from ETRPCA serves as the background data for CRD, further enhancing detection performance. Experiments on three real hyperspectral datasets and one simulated dataset demonstrate that ETRPCA-CRD outperforms several state-of-the-art algorithms, achieving superior detection accuracy and robustness. The proposed method’s ability to effectively distinguish anomalies from background data while preserving salient signals makes it a powerful tool for hyperspectral anomaly detection.