Hybrid anomaly detection method for hyperspectral images

Anomaly detection techniques have been widely studied by researchers to locate targets that stand out from their backgrounds. Low-rank and sparse matrix decomposition model (LSDM), on the other hand, is an encouraging method to take advantage of the low-rank property of hyperspectral images and extract information from both background and anomalies. In this work, a hybrid anomaly detection method is proposed by fusing LSDM model with the Laplacian matrix to distinguish anomalies effectively. The proposed method steps are twofold. First, the high dimensional data is decomposed as low-rank and sparse matrices by robust subspace recovery algorithm as preprocessing step. After the decomposition process, Mahalanobis distance is applied to the sparse part of the data. Different than previous studies, the inverse of the covariance matrix is computed by the Laplacian matrix. The proposed approach achieves the best detection results, according to the experimental findings. The superiority of the proposed algorithm is highlighted by comparing the state-of-the-art algorithms based on four hyperspectral images.