利用最长公共子序列度量线要素相似性的方法

A Geometric Similarity Measure Method of Linear Features Based on Longest Common Sequence

现有线要素相似性度量方法主要基于空间欧式距离或面积,缺少对线状要素几何形态特征相似性的考虑,在线状要素分布密集区域或地形地貌变化剧烈区域易产生度量误差。为此,提出一种基于最长公共子序列的线状要素几何相似性度量方法。利用特征描述子将线要素节点序列转化为几何特征描述序列;并利用基于控制参数的动态规划方法求解特征描述序列间的最长公共子序列,进而度量线状要素间的几何形态相似度。模拟数据试验和真实数据试验表明,该方法在保证运行效率的情况下,具有较高的正确率。

The existing similarity measure methods of linear features are mainly based on the spatial Euclidean distance or area, while they are lack of taking the geometric features into account, which may leads to measurement error in areas with intensive linear features or violent terrain changes. For this reason, a similarity measure method of linear feature based on longest common sequence is put forward in this paper. Feature descriptor is used to transform the point sequence of linear features into geometric feature description sequence, and then the dynamic programming method based on control parameters is used to solve the longest common sequence between the feature description sequences. The geometric similarity between linear features can be quantified based on the longest common sequence solution. Both simulation datasets and real datasets experiments prove that the proposed measure method ensures the operating efficiency and it has a high accuracy.