Hyperspectral Anomaly Detection via Low-Rank Decomposition and Morphological Filtering

To effectively detect anomalies and eliminate the influence of noise on anomaly detection (AD), we propose a hyperspectral AD method based on low-rank decomposition and morphological filtering (LRDMF). For one thing, given the different ways in which anomalies and noise occur in the spectral bands, a low-rank decomposition model is proposed to decompose the original hyperspectral image (HSI) into the background, anomaly, and noise components, where a superpixel segmentation method and the sparse representation (SR) model are used to construct a robust background dictionary. For another thing, considering that the anomalies in HSI possess small area characteristics, a morphological filtering method is applied to preserve the small connected components. Finally, anomalies are detected by jointly considering the LRDMF results. The experimental results conducted on two real hyperspectral datasets demonstrate that the proposed method outperforms some of the state-of-the-art methods.