Distributed blackboard architecture for multi-spectral image interpretation based on multi-agent system

For an interpretation system, a priori knowledge of the observed scene is necessary to identify objects and if necessary to determine their description. These objects are identified by comparing the extracted data from images to an a priori description of the object or object class. Therefore the use of an appropriate knowledge can efficiently reduce the complexity of matching image data to object descriptions due to different object classes, contexts and viewing conditions. Blackboard architectures are well suited to the task of selecting and applying the relevant knowledge to each situation as it is encountered. In this paper, we present a hierarchical approach based on the use of blackboard architecture and multiagent system and how to provide a convenient way for scene interpretation and modeling issued from a multispectral satellite image