Knowledge based interpretation of aerial images using multiple sensors

A knowledge based approach for the interpretation of aerial images is presented that combines cues from multiple sensors (visual, infrared, SAR). The sensor fusion is applied at object level. This allows to use prior knowledge to increase the separability of the classes. The prior knowledge is represented explicitly using semantic nets. Interpretation exploits the semantic net to control the sequence of sensor fusion mixing bottom-up and top-down strategies. The presented approach addresses the problem of uncertain and imprecise sensor data by judging the different cues based on possibility theory. Competing interpretations are stored in a search tree. An A∗-algorithm selects the most promising, i.e. best judged, interpretation for further investigation.