A hyperspectral anomaly detection algorithm based on orthogonal subspace projection

The orithogonal subspace projection (OSP) method needs all the endmember spectral information of observation area which is usually unavailable in actual situation. In order to extend the application of OSP method, this paper proposes an algorithm without any priori information based on OSP. Firstly, the background endmember spectral matrix is obtained by using unsupervised method. Then, the OSP projection operator is calculated with the background endmember matrix. Finally, the detection operator is constructed by using the projection operator, which is used to detect the hyperspectral imagery pixel by pixel. In order to increase the detection rate, local processing is proposed for anomaly detection with no prior knowledge. The algorithm is tested with AVIRIS hyperspectral data, and binary image of targets and ROC curves are given in the paper. Experimental results show that the proposed anomaly detection method based on OSP is more effective than the classic RX detection algorithm under the case of insufficient prior knowledge, and the detection rate is significantly increased by using the local processing.