25 October 2016 Anomaly detection based on PCA and local RXOSP in hyperspectral image
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Juan Lin, Juan Lin, Kun Gao, Kun Gao, Lijing Wang, Lijing Wang, Xuemei Gong, 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); doi: 10.1117/12.2243816; https://doi.org/10.1117/12.2243816
PROCEEDINGS
6 PAGES


SHARE
Back to Top