Using Lidar to Geometrically-constrain Signature Spaces for Physics-based Target Detection

A fundamental task when performing target detection on spectral imagery is ensuring that a target signature is in the same metric domain as the measured spectral data set. Remotely sensed data are typically collected in digital counts and calibrated to radiance. That is, calibrated data have units of spectral radiance, while target signatures in the visible regime are commonly characterized in units of reflectance. A necessary precursor to running a target detection algorithm is converting the measured scene data and target signature to the same domain. Atmospheric inversion or compensation is a well-known method for transforming measured scene radiance values into the reflectance domain. While this method may be mathematically trivial, it is computationally attractive and is most effective when illumination conditions are constant across a scene. However, when illumination conditions are not constant for a given scene, significant error may be introduced when applying the same linear inversion globally.