Hyperspectral images and LiDAR based DEM fusion: A multi-modal landuse classification strategy

Hyperspectral imaging based land cover/land use classification accuracy is expected to be improved by fusion with a LIDAR based Digital Elevation Model (DEM). To this end, we propose a multi-modal architecture, as well as a filtering technique extracting a shadow invariant one dimensional feature from a pixel spectrum. The proposed approach allows treating shadow and non-shadow areas separately. DEM is incorporated into this architecture through feature extraction and post classification procedures. A digital terrain model estimated from DEM is used to calculate object heights. Slope, curvature and polynomial surface fitting based features are extracted in different scales. In post classification, DEM segments and relatively high objects obtained from DEM are interpreted by superposition with the class map.