Hyperspectral Anomaly Detection via Discriminative Feature Learning with Multiple-Dictionary Sparse Representation

Most hyperspectral anomaly detection methods directly utilize all the original spectra to recognize anomalies. However, the inherent characteristics of high spectral dimension and complex spectral correlation commonly make their detection performance unsatisfactory. Therefore, an effective feature extraction technique is necessary. To this end, this paper proposes a novel anomaly detection method via discriminative feature learning with multiple-dictionary sparse representation. Firstly, a new spectral feature selection framework based on sparse presentation is designed, which is closely guided by the anomaly detection task. Then, the representative spectra which can significantly enlarge anomaly's deviation from background are picked out by minimizing residues between background spectrum reconstruction error and anomaly spectrum recovery error. Finally, through comprehensively considering the virtues of different groups of representative features selected from multiple dictionaries, a global multiple-view detection strategy is presented to improve the detection accuracy. The proposed method is compared with ten state-of-the-art methods including LRX, SRD, CRD, LSMAD, RSAD, BACON, BACON-target, GRX, GKRX, and PCA-GRX on three real-world hyperspectral images. Corresponding to each competitor, it has the average detection performance improvement of about respectively. Extensive experiments demonstrate its superior performance in effectiveness and efficiency.