Automatic interpretation of infrared and optical reconnaissance imagery.

Progress is reviewed on the development of an all source image interpretation system which exploits complementary evidence from a range of experts. This co-operation may occur between feature detectors in different bands, between detectors searching for different types of feature, or between different types of detector of the same feature. Algorithms for detecting vehicles in infrared linescan imagery give a low missed detection rate but have been found to respond falsely to: roads fragmented by trees; structures such as cylindrical storage tanks; and to corners of man made objects, such as buildings. False alarms are reduced by applying algorithms which detect subclasses of false alarms reliably i.e. buildings and storage tanks. In addition, both are features of interest in themselves, and are useful primitives in the identification of sites. The integration of depth (in the form of disparity maps) is examined as a means of reducing false building detections. Outputs from the feature detectors are combined using a simple rule-based approach, A surface based model matching technique is examined as a means of classifying the remaining vehicle candidates.