Anomaly Detection from Hyperspectral Images Using Clustering Based Feature Reduction

An anomaly detection method with a clustering based feature reduction is proposed in this paper to improve the performance of the Local RX detector. Because of high dimensionality of hyperspectral image and the low number of available samples in each local region around each testing pixel, the estimate of local covariance matrix is not possible. So, because of singularity problem, Local RX cannot use the local covariance matrix and misses the local structures of data to model the background clutter. To deal with this problem, a supervised clustering based feature reduction is introduced for extraction of background features with minimum overlap and redundant information. In the projected feature space with reduced dimensionality, the local structures of background pixels are estimated to efficiently model the background data. The experiments done on both synthetic and real hyperspectral images show the superior detection performance of the proposed method with a relatively high speed.