Hyperspectral sub-pixel target detection using hybrid algorithms and Physics Based Modeling

This thesis develops a new hybrid target detection algorithm called the Physics Based-Structured InFeasibility Target-detector (PB-SIFT) which incorporates Physics Based Modeling (PBM) along with a new Structured Infeasibility Projector (SIP) metric. Traditional matched filters are susceptible to leakage or false alarms due to bright or saturated pixels that appear target-like to hyperspectral detection algo rithms but are not truly target. This detector mitigates against such false alarms. More often than not, detection algorithms are applied to atmospherically com pensated hyperspectral imagery. Rather than compensate the imagery, we take the opposite approach by using a physics based model to generate permutations ofwhat the target might look like as seen by the sensor in radiance space. The development and status of such a method is presented as applied to the generation of target spaces. The generated target spaces are designed to fully encompass image tar get pixels while using a limited number of input model parameters. Evaluation of such target spaces shows that they can reproduce a HYDICE image target pixel spectrum to less than 1% RMS error (equivalent reflectance) in the visible and less iv than 6% in the near IR. Background spaces are modeled using a linear subspace (structured) approach characterized by basis vectors found by using the maximum distance method (MaxD). The SIP is developed along with a Physics Based Orthogonal Projection Op erator (PBosp) which produces a 2 dimensional decision space. Results from the HYDICE FR I data set show that the physics based approach, along with the PBSIFT algorithm, can out perform the Spectral Angle Mapper (SAM) and Spectral Matched Filter (SMF) on both exposed and fully concealed man-made targets found in hyperspectral imagery. Furthermore, the PB-SIFT algorithm performs as good (if not better) than the Mixture Tuned Matched Filter (MTMF).