Fusion of optical and radar data for the extraction of higher quality information

Information extraction from multi-sensor remote sensing imagery is an important and challenging task for many applications such as urban area mapping and change detection. Especially for optical and radar data fusion a special acquisition (orthogonal) geometry is of great importance in order to minimize displacement effects due inaccuracy of Digital Elevation Model (DEM) used for data orthorectification and existence of unknown 3D structures in a scene. Final data spatial alignment is performed by recently proposed co-registration method based on a Mutual Information measure. For a combination of features originating from different sources, which are quite often non-commensurable, we propose an information fusion framework called INFOFUSE consisting of three main processing steps: feature fission (feature extraction aiming at complete description of a scene), unsupervised clustering (complexity reduction and feature conversion to a common dictionary) and supervised classification realized by Bayesian/Neural networks. An example of urban area classification is presented for the orthogonal acquisition of spaceborne very high resolution WorldView-2 and TerraSAR-X Spotlight imagery over Munich city, South Germany. Experimental results confirm our approach and show a great potential also for other applications such as change detection. Additionally, simulation of optical and SAR data using high quality (Digital Surface Model) DSM in different acquisition geometries will be performed in order to make further interpretation and processing of SAR imagery easier. Learning of semantic relationships between objects in optical and radar data will help to enhance information extraction for various applications as classification, interpretation, change detection and scene reconstruction.