基于等高线空间关系的鞍部点提取方法

鞍部点是反映地表形态起伏变化的重要地形特征点之一,准确地提取鞍部点有利于地形的空间关系和结构特征分析。现有的鞍部点提取方法通常是直接基于规则格网DEM数据,无法顾及鞍部点与周围地形的空间拓扑关系和复杂地形对其的影响,不仅产生大量的伪鞍部点,而且忽略一些关键地区的鞍部点。本文根据鞍部点的地形形态特征,设计了一种基于等高线数据的鞍部点提取算法。该算法利用等高线闭合的特征,将等高线按照一定规则转成等高面数据,再利用等高面之间的相邻拓扑关系实现递归查找并自动提取鞍部点。实验结果显示:①鞍部点的数量和位置与等高距的大小显著相关,在一定尺度范围内,等高距越小,提取出鞍部点越多,位置精度也逐渐提高;②与基于规则格网DEM数据提取方法相比,该方法能更有效的过滤大量伪鞍部点,提高了鞍部点的提取精度,同时也降低了鞍部点提取算法的复杂度;与基于等高线的增量缓冲方法(Incremental Buffering Algorithm)相比,本文的方法能有效提高鞍部点提取的完整性,更适用于本文DEM的尺度即5 m DEM数据。

A Method of Saddle Point Extraction based on Contour Spatial Relationship

The saddle point is one of the important terrain feature points that reflects the fluctuation of the surface morphology. Accurate extraction of saddle points is beneficial to the analysis of spatial relationship and structural feature of terrain. Existing saddle point extraction methods are usually directly based on regular grid DEMs data which not have topological relation, unable to take into account the spatial topological relationship between saddle point and surrounding terrain and the influence of complex terrain on them. Many false feature points which are not in the saddle region will be extracted, and saddle points in some key areas also will be ignored. Thus, this paper designs an algorithm for extracting saddle points based on contour data according to the morphological characteristic of saddle points. This algorithm makes use of the feature of contour closure, converts contour lines into contour polygons data according to certain rules, and then uses the adjacent topological relations between contour polygons to recursively search and automatically extract the saddle-points. The experimental results show: (1) The number and position of saddle points are significantly related to the size of the contour interval. Within a certain range of scales, the smaller the contour interval is, the more saddle points are extracted, and the position accuracy is gradually improved. (2) Compared with the method based on regular grid DEMs data extraction, this method can more effectively filter many false feature points which are not in the saddle region, improve the accuracy of saddle point extraction, and also reduce the complexity of the saddle point extraction algorithm. In addition, DEM scale is selective for saddle point extraction. Compared with incremental contour method to extract saddle points, the idea is to find the intersection point which is the saddle point by extending the adjacent pseudo-saddle point contour line to the junction, this way has the advantage of more accurate position. However, this method requires DEM data with high precision, and when applied to 5-meter spatial resolution DEM data, most saddle points can't be extracted. The method in this paper is to use the midpoint of the nearest point in the two peaks as the saddle point, whose point position is not completely accurate, resulting in a certain error in spatial position accuracy. However, it can identify the majority of saddle points effectively, which has an advantage over the method of extracting saddle points mentioned above. Thus, the method in this paper can effectively improve the integrity of saddle point extraction and is more suitable for the DEM scale data (5 m DEM) in this paper.