Abstract
Automatic interpretation of remote sensing data gathers more and more importance for surveillance tasks, reconnaissance and automatic generation and quality control of geographic maps. Methods and applications exist for structural analysis of image data as well as specialized segmentation algorithms for certain object classes. At the Institute of Communication Theory and Signal Processing focus is set on procedures that incorporate a priori knowledge into the interpretation process. Though many advanced image processing algorithms have been developed in the past, a disadvantage of earlier interpretation systems is the missing combination capability for the results of different - especially multisensor - image processing operators. The system GeoAIDA presented in this paper utilizes a semantic net to model a priori knowledge about the scene. The low-level, context dependent segmentation is accomplished by already existing, external image processing operators, which are integrated and controlled by GeoAIDA. Also the evaluation of the interpretation hypothesis is done by external operators, linked to the GeoAIDA system. As a result an interactive map with user selectable level-of-detail is generated.
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References
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Bückner, J., Pahl, M., Stahlhut, O., Liedtke, C.E. (2002). A Knowledge-Based System for Context Dependent Evaluation of Remote Sensing Data. In: Van Gool, L. (eds) Pattern Recognition. DAGM 2002. Lecture Notes in Computer Science, vol 2449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45783-6_8
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DOI: https://doi.org/10.1007/3-540-45783-6_8
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