Anomaly detection for hyperspectral images based on improved RX algorithm

Li Ma; Jinwen Tian

doi:10.1117/12.748673

15 November 2007 Anomaly detection for hyperspectral images based on improved RX algorithm

Li Ma, Jinwen Tian

Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 67870Q (2007) https://doi.org/10.1117/12.748673
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

An improved local RX anomaly detection algorithm is proposed. It firstly projects the images onto the background orthogonal subspace to make local data closer to multivariate normal distribution. Then for every tested pixel in the center of the sliding local window, the bands used in RX detector are chosen adaptively. To avoid the influence of anomaly information on the background characteristic statistic, the anomalous pixels in the local background are removed and the covariance matrix is calculated using real background pixels. Finally the RX detector is used to calculate the anomalous degree of every tested pixel. Experimental results indicate it is robust and has good anomaly detection performances under complex unknown background.

Citation Download Citation

Li Ma and Jinwen Tian "Anomaly detection for hyperspectral images based on improved RX algorithm", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67870Q (15 November 2007); https://doi.org/10.1117/12.748673

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