Anomaly detection using spectral mismatch between anomaly pattern and its neighborhood

In this paper we present a novel algorithm for anomaly detection in multichannel images. Proposed algorithm uses spectral mismatch criterion to describe anomalous properties of small image regions. The idea behind the criterion is that the brightness of the anomalous region can't be represented as a function of pixels comprising that region. In our paper, we consider a local pattern of anomaly and its neighborhood, and we use a linear function to approximate the anomaly at each image position. In contrast to existing global and local RXD algorithms our approach allows more adaptive and noise resistant detection of anomalies. Experimental results are presented for hyperspectral remote sensing images