Map-Matching of Radar Images and Electronic Charts Using the Hausdorff Distance

This paper describes a new method of image pattern recognition based on the Hausdorff Distance. The technique looks for similarities between a given pattern and its possible representations within an image. This method performs satisfactorily when confronted to image perturbations or partial occlusions. An extension of the classical Hausdorff Distance technique chooses the best candidate among multiple suboptimal solutions. The search strategy is based on the Branch and Bounds algorithm, where cells with low probability of containing the optimal solution are pruned, while feasible cells are divided again until the optimal solution is found. By using this strategy, exhaustive and no-informative searches are avoided among the possible combinations, reducing the processing time considerably. A case study is presented, where the proposed method is applied to calibration of surveillance radars using hydrographic charts as models for the radar echo images.