Anomaly detection based on PCA and local RXOSP in hyperspectral image

Juan Lin; Kun Gao; Lijing Wang; Xuemei Gong

doi:10.1117/12.2243816

25 October 2016 Anomaly detection based on PCA and local RXOSP in hyperspectral image

Juan Lin, Kun Gao, Lijing Wang, Xuemei Gong

Proceedings Volume 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology; 1015606 (2016) https://doi.org/10.1117/12.2243816
Event: International Symposium on Optoelectronic Technology and Application 2016, 2016, Beijing, China

Abstract

Aiming at the noise vulnerability and the low detection performance of the classical RX algorithm under the complex background, an improved RX-OSP hyperspectral anomaly detection method is proposed. Firstly, PCA dimension reduction method is applied to suppress the background of hyper-spectral image. Secondly, RX operator is used to detect the pixels owning the most prominent anomaly and the pixels are projected to their orthogonal complement subspaces. Then RXOSP processing is repeated according to the foregoing steps until there is no obvious anomaly. During the process of detection, the covariance matrix is calculated by localization instead of the traditional global approach to reduce the false detection effectively. Finally, ROC curve is adopted as the evaluation index for the experiment results, which reveals that the improved RXOSP algorithm is superior to RX, PCA-RX and RXOSP algorithms.

Citation Download Citation

Juan Lin, Kun Gao, Lijing Wang, and Xuemei Gong "Anomaly detection based on PCA and local RXOSP in hyperspectral image", Proc. SPIE 10156, Hyperspectral Remote Sensing Applications and Environmental Monitoring and Safety Testing Technology, 1015606 (25 October 2016); https://doi.org/10.1117/12.2243816

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