Hyperspectral image anomaly detection based on minimum noise fraction and tensor low-rank theory

Hyperspectral imaging (HSI) provides a powerful guarantee for the accurate identification of various targets due to its rich spectral and spatial information. Anomaly detection, a significant application of HSI, is inevitably affected by noise during the imaging process. To achieve effective HSI anomaly detection in complex environments, this paper propose minimum noise fraction (MNF) and tensor low-rank anomaly detection by replacing principal component analysis (PCA) in PCA-tensor low-rank and sparse representation (TLRSR) with MNF as a preprocessing step. Experimental tests using real hyperspectral data demonstrate that the proposed method can effectively reduce noise in HSIs and significantly enhance the anomaly detection accuracy.