Automated Density Estimation in Hyperspectral Anomaly Detection

Detecting targets with unknown spectral signatures in hyperspectral imagery has been proven to be a topic of great interest in several applications. Because no knowledge about the targets of interest is assumed this task is performed by searching the image for anomalous pixels i.e. those pixels deviating from a statistical model of the background. In this thesis work a new scheme is proposed for detecting both global and local anomalies.