Anomaly detection in hyperspectral imagery: an overview

Interest on anomaly detection for hyperspectral images is increasingly growing the last decades due to the diversity of applications that aims for detecting small distinctive objects dispersed in a large geographic zone, without any prior knowledge about the scene. In addition to the absence of prior knowledge, many problems are particularly challenging for the anomaly detection such as the differentiation between real targets, false alarms and noise, the detection of anomaly of different shapes and sizes, and the high computational cost of the proposed approaches. This overview presents the literature and approaches proposed to address these issues. These approaches are grouped into four categories based on the underlying techniques used to achieve the detection: 1) statistical methods, 2) kernel based methods, 3) projection based methods and 4) segmentation based methods.