Hyperspectral Anomaly Detection on the Sphere

Joana Frontera-Pons

Abstract: Anomaly detectors aim at finding any pixel that is different from its surrounding normal background pixels. Most of the classical anomaly detection algorithms are based on the Mahalanobis distance, and therefore, they are mainly sensitive to the signal energy. One could project the hyperspectral dat-acube onto the unit hypersphere in order to enhance detection for faint targets. In this context, we introduce the class of Angular Gaussian distributions for hyperspectral data modelling. Moreover, the corresponding maximum likelihood estimates and the generalized likelihood ratio test are both investigated. The resulting anomaly detection scheme is independent on the true distribution of the data within the family of elliptical distribuions. These results are illustrated through simulations and over one real hyperspectral image.