Multiple Window Anomaly Detection

Abstract

Because of advances in hyperspectral imaging sensors, many unknown and subtle targets that cannot be resolved by multispectral imagery can now be uncovered by hyperspectral imagery. These targets generally cannot be identified by visual inspection or prior knowledge, but yet provide important and useful information for data exploitation. One such type of targets is anomalies which have recently received considerable interest in hyperspectral image analysis. Many anomaly detectors have been developed and most of them are based on the most widely used Reed–Yu’s algorithm, called RX Detector (RXD) (Reed and Yu 1990), referred to as the sample covariance matrix K-based Anomaly Detector (K-AD) in Chap. 5. However, a key issue in making RX detector-like anomaly detectors effective is how to utilize effectively the spectral information provided by data samples, e.g., sample covariance matrix used by K-AD. Recently, a Dual Window-based Eigen Separation Transform (DWEST) was developed by Kwon and Narsabadi (2003) to address this issue. This chapter extends the concept of DWEST to develop a new approach, to be called multiple-window anomaly detection (MWAD) by making use of multiple windows to perform anomaly detection adaptively. As a result, MWAD is able to detect anomalies of various sizes using multiple windows so that local spectral variations can be characterized and extracted by different window sizes. By virtue of this newly developed MWAD, many existing K-AD-like anomaly detectors including DWEST can be derived as special cases of MWAD.