Hyperspectral anomaly detection via low-rank and sparse decomposition with cluster subspace accumulation

Anomaly detection (AD) has emerged as a prominent area of research in hyperspectral imagery (HSI) processing. Traditional algorithms, such as low-rank and sparse matrix decomposition (LRaSMD), often struggle to effectively address challenges related to background interference, anomaly targets, and noise. To overcome these limitations, we propose a novel method that leverages both spatial and spectral features in HSI. Initially, the original HSI is segmented into several subspaces using the k-means method, which reduces redundancy among HSI bands. Subsequently, the fractional Fourier transform (FrFT) is applied within each subspace, enhancing the distinction between background and anomaly target information while simultaneously suppressing noise. To further improve the stability and discriminative power of the HSI, LRaSMD is employed. Finally, the modified Reed-Xiaoli (RX) detector is utilized to identify anomalies within each subspace. The results from these detections are then aggregated to produce a comprehensive final outcome. Experiments conducted on five real HSI data sets yield an average area under the curve (AUC) of 0.9761 with a standard deviation of 0.0156 for the proposed algorithm. These results indicate that our method is highly competitive in the field of anomaly detection.