Spectral Anomaly Detection Based on Dictionary Learning for Sea Surfaces

Anomalies in remote sensing images are generally reflected in two aspects of spatial and spectral ones, as for the anomaly detection of sea surface using multispectral or hyperspectral images, spectral information is more important. To this end, a novel spectral anomaly detection method based on dictionary optimization is proposed in this letter. More specifically, the normal scene is first defined to distinguish the anomaly. Then, without any assumption about the distribution of anomaly, a spectral dictionary is formulated and derived theoretically with optimization to express the normal scenes. Using the sparse and low-rank constraints, the alternating direction method of multiplier (ADMM) is employed to solve the above optimization in the spectral domain. Finally, for a given sea-surface image to be detected, the error matrix that cannot be fully expressed by the optimized spectral dictionary is regarded as anomalies. It shows certain generality for various kinds of spectral anomalies on the sea surface. Taking multispectral images obtained by the HY-1C satellite as an example, comparisons with related state-of-the-art methods demonstrate that our proposed method achieves the best anomaly detection performance not only for oil-spill pollution but also for algae pollution.