Toward a semi-automatic interpretation of scenes issued from multisensor satellite images

With the rapid development in remote sensing, digital image processing becomes an important tool for quantitative and statistical interpretation of remotely sensed images. These images, often, contain complex and natural scenes. The constant increase in the amount of data to treat issued from satellites images, has made automatic content extraction and retrieval highly desired goals for effective and efficient processing of remotely sensed imagery. One of the main difficulties of these applications is the knowledge representation of objects, scene and interpretation strategy. In this paper, we present an integrated hierarchical approach based on the use of a hierarchical blackboard architecture and multi-agent system in order to increase the degree of semi-automatic interpretation of remotely sensed images. This hierarchical architecture is motivated in order to avoid the bottleneck caused by the growing number of the knowledge sources on a single blackboard, reduce the information complexity and complex tasks and increase the system efficiency whenever the information is distributed over several blackboard levels. In this paper, a stage of image analysis has been examined in order to establish the viability of MAS and hierarchical blackboard architecture for change detection. A set of Spot multi-temporal images, was analyzed in terms of spectral responses from different land cover types.