LAUSR

Assessment of damages due to fire, drought, flood, land slide, etc., using hyperspectral images from Hyperion, AVIRIS or HyspIRI has challenging issues. The effects of different illumination, atmospheric conditions and varying sensor/target viewing geometries are some of these challenges. A common approach for target detection is to apply atmospheric correction algorithms to the radiance image data cube and then search within the atmospherically corrected image cube for the target reflectance signature of interest. One major issue with the above approach is that it is computationally demanding. In this paper, instead of applying atmospheric correction to the raw radiance data, we generate radiance profiles of burn scar for the observed atmospheric and illumination conditions at the time of the hyperspectral image data collection and form a radiance profile library using a nonlinear analytical model for radiative transfer and MODTRAN. The target detection has been performed by a spectral similarity technique which takes into consideration multiple radiance profile variants of the target of interest. The effectiveness of the radiance domain-based target detection approach on reducing the computation time has been demonstrated on burn scar detection using airborne AVIRIS image data.