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In this paper, a spectral–spatial anomaly detection method based on tensor de-composition is proposed. Firstly, tensor data is used to represent hyperspectral data to retain its original spectral and spatial information. Second, this method reconstructs the hyperspectral data into low-rank tensors and sparse tensors. This method uses the weighted tensor Schatten-p norm minimization (WTSNM) to stand for rank minimization. WTSNM treats different singular values differently. Finally, the reconstructed sparse tensor is used as input data and the LRX method is used to detect abnormal targets. As this approach can effectively utilize the spectral information and spatial information of hyperspectral images, it greatly improves detection accuracy, experimental results on five real data sets demonstrate that the proposed method outperforms several state-of-the-art algorithms.

Enhance tensor RPCA-LRX anomaly detection algorithm for hyperspectral image