An image segmentation approach for improving target detection in a 3-D signal processor

The detection of small, weak targets collected from electro-optical radiation is a challenging problem, particularly in the presence of nonstationary backgrounds. In this paper, we propose a theoretical justification for the loss in performance of slowly moving targets in regions of benign clutter. In particular, a K-means segmentation technique is developed using a fixed number of classes and a variety of local scene features. This class map is used by a 3-D matched filter to estimate a covariance matrix for each region. The filter would then whiten each region using the appropriate class map. The algorithm is applied in this paper to actual sensor data which contains heterogeneous scenes taken from the Airborne Infrared Measurement Systems (AIRMS) sensor. Performance is assessed through the measure of SNRs and receiver operating characteristic (ROC) curves based on a suite of injected targets.