Urban Scene Labeling Based on Multi-Modal Data Acquired from Aerial Sensor Platforms

In this paper, we address urban scene interpretation on the basis of multi-modal data acquired from aerial sensor platforms. These data comprise RGB color information, hyperspectral information and 3D shape information. As hyperspectral data are known to contain a high degree of redundancy which, in turn, might affect the quality of derived classification results, we also involve techniques for dimensionality reduction and feature selection as well as a transformation of hyperspectral data to high-resolution multispectral Sentinel-2-like data. We use the different types of data to define sets of radiometric and geometric features which are provided separately and in different combinations as input to a Random Forest classifier. To assess the potential of the different types of data and their combination for urban scene interpretation, we present results achieved for the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set.