A novel hyperspectral image anomaly detection method based on low rank representation

This paper presents a novel method for anomaly detection in hyperspectral image(HSI) based on low-rank representation. In the observed HSI, the anomalies can be separated from the background. Since each pixel in the background can be approximately represented by a background dictionary, and the representation coefficients of the background pixels are correlative, a low-rank representation model is used to model the background part. Besides, to gain robust representation coefficient, the sum-to-one constraint is added. The advantage of the proposed low-rank representation sum-to-one (LRRSTO) method is that it makes use of the global correlation of the background and strength the robustness of the representation. Experiments results have been conducted using both simulated and real data sets. These experiments indicated that our algorithm achieves very promising performance.