Hyperspectral anomaly detection using background learning and structured sparse representation

A novel background dictionary learning and structured sparse representation based anomaly detection method is proposed for hyperspectral imagery. First, a robust PCA spectrum dictionary is learned from the plausible background area detected by the local RX detector. With the learned dictionary, the reweighted Laplace prior based structured sparse representation model is then employed to reconstruct the spectrum of each pixel in the image. Due to considering the structured sparsity in representation, the background spectra can be reconstructed more accurately than anomaly ones. Thus, reconstruction error is utilized to separate the anomaly pixels and background ones. Experimental results on both simulated and real-world datasets demonstrate that the proposed method outperforms several state-of-the-art hyperspectral anomaly detection methods.