Target detection in a structured background environment using an infeasibility metric in an invariant space

This paper develops a hybrid target detector that incorporates structured backgrounds and physics based model- ing 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 op- posite 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 as 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. Background spaces are modeled using a linear subspace (structured) approach characterized by endmembers found by using the maximum distance method (MaxD). After augmenting the image data with the target space, 15 endmembers were found, which were not related to the target (i.e., background endmembers). A geometric infeasibility metric is developed which enables one to be more selective in rejecting false alarms. Preliminary results in the design of such a metric show that an orthogonal projection operator based on target space vectors can distinguish between target and background pixels. Furthermore, when used in conjunction with an operator that produces abundance-like values, we obtained separation between target, background, and anomalous pixels. This approach was applied to HYDICE image spectrometer data.