Finding Hyperspectral Anomalies Using Multivariate Outlier Detection

This research demonstrates the adverse implications of using non-robust statistical methods for detecting anomalies in hyperspectral image data, and proposes the use of multivariate outlier detection methods as an alternative detection strategy. Existing outlier detection methods are adapted for use in a hyperspectral image context, and their performance is compared to the benchmark RX detector and a cluster-based anomaly detector. Tests conducted using both simulated data and actual hyperspectral imagery indicate that multivariate outlier detection methods can achieve superior detection performance relative to current non-robust detection methods.