KNOWLEDGE BASED INTERPRETATION OF MULTISENSOR AND MULTITEMPORAL REMOTE SENSING IMAGES

The increasing amount of remotely sensed imagery from multiple platforms requires efficient analysis techniques. The leading id a of the presented work is to automate the interpretation of multisensor and multitemporal remote sensing images by the use of common pr ior knowledge about landscape scenes. The presented system is able to use specific map knowledge of a geoinformation system (GIS), information about sensor projections and temporal changes of scene objects. The prior knowledge is represented explicitly by a semantic net. A common concept has been developed to distinguish within the knowledge base between the semantics of objects and their vi ual appearance in the different sensors considering the physical principle of the sensor and the material and surface properties of the objects. In this presentation, the basic structure of the system and its use for sensor fusion on different structural and functional le vels s presented. Results are shown for the extraction of roads from multisensor images. The approach for the analysis of multitemporal images is illustrated for the interpretation of an industrial fairground.