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The remote detection of minefields has a high priority with the armed forces of several countries, including Canada. The Defence Research Establishment Suffield and the University of British Columbia are jointly developing methods to identify surface-laid minefields by real- time analysis of airborne sensor imagery. Passive multispectral imaging has been investigated, but the problem of analyzing monochromatic images from active infrared scanners is considered here. Among the severe constraints are that one must find clusters of bright regions each only several pixels wide, sparsely distributed over large areas. Also, the sharply forward peaked scattering from the mines and variable terrain roughness leads to the result that some mines are not detected. The required very high input data rate suggests simple, high speed target cuing which is amenable to hardware implementation. This is incompatible with slower image understanding techniques needed to identify mines by their shapes, and minefields by the spatial relationships between mines. A hierarchical algorithm is thus presented as a solution to the problem. In progressing from lower to higher levels, computational operations become more complex and less able to be implemented on conventional high-speed devices but the volume of data to be analyzed decreases. At the lowest level, non-suspect regions are rejected to drastically reduce the data rate. At the next higher level, suspect regions are segmented into homogeneous sub-regions. The next level consists of extraction of morphological features of the sub-regions. One level higher, sub-regions are classified based on extracted features. At the top level, spatial relationships between `mine-like' regions are determined and are used by knowledge-based methods to classify the imaged area as being a minefield with a specified likelihood. The algorithm, which includes a novel region segmenter, has been developed up to and including sub-region classification. Studies using simulated yet realistic thermal infrared minefield images have demonstrated that the algorithm is successful in detecting individual mines. Current efforts to make the algorithm execute in real-time, by implementing it on a transputer network communicating with array processors, will also be discussed.
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