利用改进的Hausdorff距离匹配多尺度线要素OACSTPCD

In the matching process of multi-scale linear feature,traditional Hausdorff distance is prone to be affected by noisy data.In order to improve the robustness of algorithm,we proposed an improved Hausdorff distance algorithm.Considering the problem of partial matching,we used the curve segmentation algorithm to segment the long curve with short curve.Aiming at the difference problem of point distribution,we used the curve encryption algorithm to gather the curve with the center node.With respect to the noise magnitude of curve points set,taking the median of distance set as the similarity index,we judged whether the matching features were the same.We selected different scales of administrative boundaries and road network data to validate the performance of proposed algorithm.The result indicates that the algorithm can effectively solve the matching problem of multi-scale linear feature.