Operational and Performance Considerations of Radiative-Transfer Modeling in Hyperspectral Target Detection
Tracking of vehicles, in both radiance and reflectance hyperspectral imagery, was analyzed using traditional reflectance domain and forward predicting physics based target detection algorithms. The investigation consisted of locating vehicles in one image followed by locating the same (relocated) vehicles in another image that was collected at a different time. Traditional approaches to hyperspectral target detection involve the application of detection algorithms to atmospherically compensated imagery. Rather than compensate the imagery, a more recent approach uses physical models to generate radiance signature spaces. The signature space is actually a representation of what the target might look like to the sensor as the reflectance propagates through the atmosphere. The model takes into account atmospherics, illumination conditions and target reflectivity. Different collection times have varying atmospheric conditions leading to changes in the sensor-reaching radiance spectrum. A forward predicting physical model, applied in the radiance domain, handles this variation. The data used in this study is a new ground truthed hyperspectral data set, collect with the airborne HyMap sensor, and which is now freely available to the community through the web for evaluation, testing, and algorithm development. Vehicles were relocated across various flight lines. This paper compares vehicle detection results, across these multiple images collected within a short time period, using both the physical model and the traditional atmospheric compensation approach.