Multiple-Window Anomaly Detection for Hyperspectral Imagery

Abstract

Anomaly detection is of particular interest in hyperspectral image analysis since many unknown and subtle signals which cannot be resolved by multispectral sensors can now be uncovered by hyperspectral imagers. More importantly, the signals of this type generally cannot be identified by visual assessment or prior knowledge and provide crucial and critical information for data analysis. Many anomaly detectors have been designed based on the most widely used anomaly detector developed by Reed and Yu, called RX detector (RXD). However, a key issue in making RX detector-like anomaly detectors successful is how to effectively utilize the information provided by the sample correlation, e.g., sample covariance matrix used by RXD. This paper develops a concept of designing anomaly detectors which includes RXD-like anomaly detectors as special cases. It is referred to as multiple-window anomaly detection (MWAD) which makes use of multiple windows with varying sizes to capture different levels of local spectral variations so that anomalous targets of various sizes can be characterized and interpreted by different window sizes. With this new MWAD, many interesting findings can be derived including the RXD-like anomaly detectors as its special cases.