中文摘要：

Li DAR点云为小尺度地表形态的提取与表达提供了精确的数据源,但其高密度性与不确定性,导致应用Morse理论提取的特征点中含有大量的＂伪特征点＂。这里首先通过定义特征点指数等一系列概念,模拟特征点周围区域的地表形态,建立特征点重要性度量指标与计算方法;然后给出了地表重要特征点的提取算法;最后,进行了试验验证与分析。结果表明：提出的算法优于现有的持续值法与自然法则法,可以有效剔除＂伪特征点＂,实现基于Li DAR点云小尺度复杂地形的特征点精确提取与多层次表达。

英文摘要：

Enough data sources for extracting and expressing the small-scale terrain surface accurately are provided by Li DAR points cloud,but much pseudo feature points are introduced into for their high density and uncertainty when extracting critical points based on Morse theory. Firstly, a series of definitions were given, such as featurepoints index, to simulate the terrain surface of the surrounding area of the feature points and establish the measurement of the importance of the feature points. Then an algorithm for extracting important feature points was proposed.Finally,the algorithm was validated experimentally and analyzed. The results showed that the proposed algorithm was superior to the persistence algorithm and the nature principle algorithm. The algorithm could not only eliminate the pseudo feature points effectively,but also be able to extract feature points accurately and realize the multi-level representation of the small-scale terrain based on Li DAR point cloud data.