Mitigation of Correlation and Heterogeneity Effects in Hyperspectral Data

The RX anomaly detector is well known for its unsupervised ability to detect anomalies in hyperspectral images (HSI). However, the RX method assumes the data is uncorrelated and homogeneous, both of which are not inherent in HSI data. To defeat the correlation and homogeneity, a new method dubbed Iterative Linear RX is proposed. Rather than the test pixel being inside a window used by RX, Iterative Linear RX employs a line of pixels above and below the test pixel. Through the use of Receiver Operating Characteristic (ROC) curves, this paper presents Iterative Linear RX alongside the standard RX algorithm, the newly introduced Iterative RX, a successful advancement to the benchmark RX HSI detector, and the global Support Vector Data Description (SVDD) algorithm, a promising new HSI detector, to show the results of the newly proposed method.