Anomaly Discrimination in Hyperspectral Imagery

Anomaly detection finds data samples whose signatures are spectrally distinct from their surrounding data samples. Unfortunately, it cannot discriminate the anomalies it detected one from another. In order to accomplish this task it requires a way of measuring spectral similarity such as spectral angle mapper (SAM) or spectral information divergence (SID) to determine if a detected anomaly is different from another. However, this arises in a challenging issue of how to find an appropriate thresholding value for this purpose. Interestingly, this issue has not received much attention in the past. This paper investigates the issue of anomaly discrimination which can differentiate detected anomalies without using any spectral measure. The ideas are to makes use unsupervised target detection algorithms, Automatic Target Generation Process (ATGP) coupled with an anomaly detector to distinguish detected anomalies. Experimental results show that the proposed methods are indeed very effective in anomaly discrimination.