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 pace. 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.
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 5 (2006) V-1193- V-1196
RIT – Main Campus