Physics Based Target Detection Using a Hybrid Algorithm With an Infeasibility Metric

This paper develops (and applies) a hybrid target detector that incorporates structured backgrounds and physics based modeling together with a geometric infeasibility metric. More often than not, detection algorithms are usually applied to atmospherically compensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations of what the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented and applied to the generation of target spaces. The generated target spaces are designed to fully encompass image target pixels while using a limited number of input model parameters. Additionally, a structured infeasibility projector (SIP) is developed which enables one to be more selective in rejecting false alarms. Results on HYDICE data show that the SIP algorithm, in conjunction with a physics based detector, outperforms results from the SAM and SMF algorithms for a target that is both fully sunlit and obscured by a tree canopy