Abstract
It is well known that background characteristics have an impact on target signature characteristics. There are many types of backgrounds that are relevant for military application purposes; e. g. wood, grass, urban, or water areas. Current algorithms for automatic target detection and recognition (ATR) usually do not distinguish between these types of background. At most they have some sort of adaptive behavior. An important first step for our approaches is the automatic geo-coding of the images. An accurate geo-reference is necessary for using a GIS to define Regions of Expectations (ROE-i.e. image background regions with geographical semantics and known signature characteristics) in the image and for fusing the (multiple) sensor data. These ROEs could be road surfaces, forest areas or forest edge areas, water areas, and others. The knowledge about the background characteristics allows the development of a method base of dedicated algorithms. According to the sensor and the defined ROEs the most suitable algorithms can be selected form the method base and applied during operation. The detection and recognition results of the various algorithms can be fused due to the registered sensor data.