We describe a method of acquiring ground vehicles in cluttered
environments by making use of three dimensional geometrical
features. Our approach exploits a wide spectrum of shape and structure attributes to achieve very high performance target detection and scene interpretation capabilities. The system we present begins with extraction of terrain from an observed scene, and proceeds to cluster the remaining points into macroscopic objects. These objects are then subjected to a series of tests that use textural and structural measures at multiple scales to discriminate targets from natural and manmade clutter. We present experimental results on a new set of synthetic three-dimensional data, demonstrating excellent target detection and false alarm suppression performance.
In this paper we present a method of fusing evidence of targets in an observed SAR image. We have selected three simple feature types to perform these initial experiments. Fusion is performed by straightforward addition of log likelihoods over feature match types. In all cases among the three feature types tested it is observed that the probability of identification improves as feature types are added. The method of recognition is based on the probabilistic distance transform (PDT). This approach derives from traditional distance transform (DT) methods of matching target predictions (based either on training or model based predictions) to observed features. The PDT method retains the basic DT matching structure, including the advantages of fast processing and non-unique correspondences between predicted and observed features, while interpreting 'distance' in terms of spatial probability densities of predicted and observed features. The PDT matching approach then results in a statistic that can be treated as a likelihood of match between an observed set of features and a predicted target signature.
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