Multi-sensor remote sensing information fusion for urban area classification and change detection

Abstract

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. A special acquisition (orthogonal) geometry is of great importance for optical and radar data fusion. This acquisition geometry allows to minimize displacement effects due inaccuracy of Digital Elevation Model (DEM) used for data ortho-rectification 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 representation in a common dictionary) and supervised classification realized by Bayesian or Neural networks. An example of urban area classification is presented for the orthogonal acquisition of space borne 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.