A hyperspectral anomaly detection algorithm based on orthogonal subspace projection

Ying Liu; Kun Gao; Lijing Wang; Youwen Zhuang

doi:10.1117/12.2072616

24 November 2014 A hyperspectral anomaly detection algorithm based on orthogonal subspace projection

Ying Liu, Kun Gao, Lijing Wang, Youwen Zhuang

Author Affiliations +

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93012E (2014) https://doi.org/10.1117/12.2072616
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

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.

Citation Download Citation

Ying Liu, Ying Liu, Kun Gao, Kun Gao, Lijing Wang, Lijing Wang, Youwen Zhuang, Youwen Zhuang, "A hyperspectral anomaly detection algorithm based on orthogonal subspace projection", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012E (24 November 2014); doi: 10.1117/12.2072616; https://doi.org/10.1117/12.2072616

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