Interpretation of remotely sensed images in a context of multisensor fusion using a multi-specialist architecture

This report presents a scene interpretation system in a context of multisensor fusion ; it has been applied to the interpretation of remootely sensed images. First we present a typology of the multisensor fusion concepts involved, and we derive the consequences of modeling problems for objects, scene and strategy. The proposed multi-specialist architecture generalizes the ideas of our previous work by taking into account the knowledge about sensors, the multiple viewing notion (shot), and the uncertainty and impresision of models and data modeled with the possibility theory. In particuliar, genetic models of objetcs are represented by concepts independent of sensors (geometry, materials and spatial context). Three kinds of specialists are present in the architecture : generic specialists (scene and conflict), semantic objetc specialists and low level specialists. A blackboard structure with a centralized control is used. The interpreted scene is implemented as a matrix of pointers enabling conflicts to be detected very easily. Under the control of the scene specialist, the conflict specialist resolves conflicts using the spacial context knowledge of objetcs. Finally, an interpretation system with SAR/SPOT sensors is described, and an example of a session concerned with bridge, urban area and road detection is shown