Anomaly detection algorithm based on NSCT and spatial clustering in hyperspectral imagery

Due to the interference of complex background information, anomaly detection algorithm has incremental false alarm rate.In order to overcome this problem, this paper proposes an improved SVDD algorithm combining the nonsubsampled contourlet transform (NSCT) with spatial clustering.Hyperspectral imagery is transformed by NSCT, and the low frequency image containing most background information is obtained.The background residual error which is the minus of the hyperspectral imagery and low frequency image can be acquired, whereupon the background information is suppressed.Then, the low frequency image is clustered by spatial clustering method, thereupon the feature spectrum of each sub-region is computed and used as a training sample for SVDD.Hence it can eliminate the influence induced by the anomalous spectrum or random noise, and the calculated amount is also reduced at the same time.Finally, the SVDD model is used to detect background residual error data.The results show that the proposed method can inhibit the interference of complex background.It has lower false alarm rate, and hence it is more appropriate for global anomaly detection in hyperspectral imagery.