Spatial-spectral Extraction for Hyperspectral Anomaly Detection

A novel hyperspectral anomaly detection method is proposed in this paper. This method is motivated by two important observations. First, there are hundreds of bands in a hyperspectral image (HSI), among which redundant bands and some noisy bands could be eliminated to increase the discriminability from the spectral domain. Meanwhile, considering the small amount of the anomalies, they may be neglected in the spatial clustering process, and the anomalies can be highlighted by eliminating the clustered result from the original HSI. In this way, the spatial-spectral extraction is proposed to detect the anomalies in the HSI. Firstly, an iterative optimal neighborhood reconstruction method is utilized to select the bands while preserving their divergence. Secondly, the spatial clustering is achieved by minimizing the intra distance in any given category. Finally, the spatial clustered HSI is eliminated from the band selected HSI, and acts as the direct input for the detection process. Experimental results and data analysis have demonstrated the effectiveness of the proposed method.