This paper presents a total system approach to image exploitation. An ensemble of auto-matic object recognizers (AOR) is described, each with a unique partition of the image being exploited. These AORs are individually tailored to a specific task, thereby limiting their computational requirements. Understanding the contextual content of the image is an important feature of this strategy. The extent of this contextual knowledge is used to partition the image, thereby restricting the range of each AOR. An autonomous navigation rule system is involved to register the image with a prior scene model knowledge base. This strategy uses a knowledge of sensor geometry and geographic area to determine an estimate of the absolute scene location. Image registration is refined by deriving a scene model of the image under exploitation. Performing a minimum distance graph measure determines the difference between the derived key scene features and the stored feature set in the knowledge base. An absolute scene partition that subdivides the terrain into regions used to direct the AORs is recorded along with the matching scene graph in the knowledge base. This strategy permits exploitation of imagery from various sensors regardless of the type of sensor used to build the knowledge base.