Fusion of spectral reflectance and derivative information for robust hyperspectral land cover classification

Developments in sensor technology have made high resolution hyperspectral remote sensing data available to the remote sensing analyst for ground cover classification and target recognition tasks. Further, with limited ground-truth data in many real-life operating scenarios, such hyperspectral classification systems often employ dimensionality reduction algorithms. In this thesis, the efficacy of spectral derivative features for hyperspectral analysis is studied. These studies are conducted within the context of both single and multiple classifier systems. Finally, a modification of existing classification techniques is proposed and tested on spectral reflectance and derivative features that adapts the classification systems to the characteristics of the dataset under consideration. Experimental results are reported with handheld, airborne and spaceborne hyperspectral data. Efficacy of the proposed approaches (using spectral derivatives and single or multiple classifiers) as quantified by the overall classification accuracy (expressed in percentage), is significantly greater than that of these systems when exploiting only reflectance information.