IEEE DATA FUSION CONTEST: GEOSPATIAL 2D AND 3D OBJECT-BASED CLASSIFICATION AND 3D RECONSTRUCTION OF ISO-CONTAINERS DEPICTED IN A LIDAR DATA SET AND AERIAL IMAGERY OF A HARBOR

Within the 2015 IEEE GRSS Data Fusion Contest extremely high resolution LiDAR point cloud must be fused with multi-spectral aerial imagery. We propose an innovative geospatial 2D and 3D object-based classification system capable of counting two populations of ISO-containers (estimated based on their standard size) located in the harbor area depicted by the two test datasets. The degree of novelty of the proposed classification system is twofold. First, it combines inductive (bottom-up, data-driven) and deductive (top-down, prior rule-based) inference mechanisms, where the latter initializes the former in a hybrid inference framework. Second, it is provided with feedback loops, which increase its robustness to changes in input data and augment its degree of automation. The geospatial outcome are tangible vector objects, which allow estimation of statistics per container together with a detailed reconstruction of the 3D scene in a GIS system.