一种基于斜率差和方位角的矢量数据匹配算法

矢量空间数据同名特征点搜索和同名特征点匹配是对多时相、多尺度地理实体要素进行变化检测的关键技术。结合矢量空间数据的坐标特征和方位角,提出一种同名地理实体要素特征点搜索与匹配算法。该算法采用分步取点的思想,分别提取曲线X、Y方向上的极值点为初始特征点,利用各极值点斜率差的绝对值作为约束条件删除冗余极值点。在上述提取结果中,两个相邻特征点通常存在较大变形,需添加部分合理特征点。综合利用特征点坐标方位角和距离进行同名特征点相似度匹配,分别利用线实体和面实体进行试验验证。结果表明:本研究算法能够适用于线实体和面实体特征点的提取与匹配,并具有良好的精度和可行性。

A vector data matching algorithm based on slope difference and azimuth

Vector spatial data with the correspondence feature point searching and the correspondence feature point matching is the key technology for multi-temporal and multi-scale geographic entity element to detect change. The research proposed a correspondence geographic entity element feature point searching and matching algorithm based on the coordinate characteristic and azimuth of vector spatial data. Firstly, the algorithm adopted the idea of taking point step by step: extracted extreme point from the X, Y direction of curve as initial feature points respectively; used the absolute value of the slope difference of each extreme point as constraint condition to remove the redundant extreme point; in the above extraction result, there might be a large deformation between the two adjacent feature points. Secondly, utilized coordinate azimuth and distance of the feature point synthetically to match the similarity of the correspondence feature point. Finally, the experimental verification was carried out by using the line entity and the surface entity. The result showed that the algorithm could be applied to the extraction and matching of the feature point of the line entity and the surface entity, and it had good accuracy and feasibility.