INFORMATION EXTRACTION USING OPTICAL AND RADAR REMOTE SENSING DATA FUSION

摘要

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 displacements due to an inaccuracy of the Digital Elevation Model (DEM) used for data ortho-rectification and due to the presence of unknown 3D structures in a scene. Final data spatial alignment is performed manually using ground control points (GCPs) or by a recently proposed automatic co-registration method based on a Mutual Information measure. These data preprocessing steps are of a crucial importance for a success of the following data fusion. 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 for complete description of a scene), unsupervised clustering (complexity reduction and feature conversion to a common domain) and supervised classification realized by Bayesian/Neural/Graphical networks. Finally, a general data processing chain for multi-sensor data fusion is presented. Examples of buildings in an urban area are presented for very high resolution space borne optical WorldView-2 and radar TerraSAR-X imagery over Munich city, Germany in different acquisition geometries including the orthogonal one/Additionally, theoretical analysis of radar signatures of buildings in urban area and its impact on the joint classification or data fusion is discussed.