Anomaly detection in hyperspectral images based spatial spectral classification

Anomaly detection in hyperspectral images aims at detecting small size objects of unknown spectra. The major problem with anomaly detection is the absence of prior knowledge. Consequently, the extraction of true anomalies from the background and noise is a challenging task. In fact, the image scene already contains the background, noises and anomalous pixels and even in presence of prior knowledge, the differentiation between these contents is often challenging and can lead to a high false alarm rate. In this paper, a new approach for anomaly detection is proposed. The approach aims at generating knowledge about the scene before anomaly detection. This knowledge is derived from a semi-supervised SVM classification based on the betweenness centrality clustering of the spatial and spectral graph of the image. Anomaly detection is performed then, based on the Mahalanobis distance between different classes of the image. Our experimental results show improvement in the detection rate compared to the benchmark anomaly detectors.