Towards the mitigation of correlation effects in anomaly detection for hyperspectral imagery

This research involves simulating remote sensing conditions using previously collected hyperspectral imagery (HSI) data. The Reed–Xiaoli (RX) anomaly detector is well-known for its unsupervised ability to detect anomalies in hyperspectral images. However, the RX detector assumes uncorrelated and homogeneous data, both of which are not inherent in HSI data. To address this difficulty, we propose a new method termed linear RX (LRX). Whereas RX places a test pixel at the center of a moving window, LRX employs a line of pixels above and below the test pixel. In this paper, we contrast the performance of LRX, a variant of LRX called iterative linear RX (ILRX), the recently introduced iterative RX (IRX) algorithm, and the support vector data description (SVDD) algorithm, a promising new HSI anomaly detector. Through experimentation, the line of pixels used by ILRX shows an advantage over RX and IRX in that it appears to mitigate the deleterious effects of correlation due to the spatial proximity of the pixels; while the iterative adaptation taken from IRX simultaneously eliminates outliers allowing ILRX an advantage over LRX. Such innovations to the basic RX algorithm allow for the reduction of bias and error in the estimation of the mean vector and covariance matrix, thus accounting for a portion of the spatial correlation inherent in HSI data.