A semantic approach to the efficient integration of interactive and automatic target recognition systems for the analysis of complex infrastructure from aerial imagery

The analysis of complex infrastructure from aerial imagery, for instance a detailed analysis of an airfield, requires the interpreter, besides to be familiar with the sensor's imaging characteristics, to have a detailed understanding of the infrastructure domain. The required domain knowledge includes knowledge about the processes and functions involved in the operation of the infrastructure, the potential objects used to provide those functions and their spatial and functional interrelations. Since it is not possible yet to provide reliable automatic object recognition (AOR) for the analysis of such complex scenes, we developed systems to support a human interpreter with either interactive approaches, able to assist the interpreter with previously acquired expert knowledge about the domain in question, or AOR methods, capable of detecting, recognizing or analyzing certain classes of objects for certain sensors. We believe, to achieve an optimal result at the end of an interpretation process in terms of efficiency and effectivity, it is essential to integrate both interactive and automatic approaches to image interpretation. In this paper we present an approach inspired by the advancing semantic web technology to represent domain knowledge, the capabilities of available AOR modules and the image parameters in an explicit way. This enables us to seamlessly extend an interactive image interpretation environment with AOR modules in a way that we can automatically select suitable AOR methods for the current subtask, focus them on an appropriate area of interest and reintegrate their results into the environment