Geometric registration and classification of hyperspectral airborne pushbroom data

Innovative geometric modeling techniques involving stochastic constraints and linear feature exploitation have been demonstrated to yield good results in the rectification of hyperspectral airborne pushbroom imagery. The unique aspects of the platform trajectory are particularly well addressed by these techniques. Supervised statistical pattern recognition techniques have been developed specifically to address the unique and challenging aspects of high dimensional data. These have resulted in processing strategies which are compatible with common desktop computing resources. The combination of such thematic class extraction with effective rectification algorithms delivers a powerful tool into the hands of those building urban databases.