Point cloud contains a large collection of point data as surface returns to describe the multidimensional aspect of the topographic features. Data points within these point clouds at the atomic level convey little or no information about the structural, physical, and spatial information about objects of interest within the scene in its raw form. Analyzing the surface described as a set of the point is complex and challenging. This paper describes the framework and methods for analyzing topographical reliefs and terrain regions to extract surface features using spatial clustering and aggregation of point cloud data. The method also describes the effectiveness of Kd- tree and nearest neighbor estimates in segmenting and visualizing the surface topographical terrain and relief region. The morphological correspondence between the points within clusters and the corresponding surface is demonstrated. It is also defined as clusters and spatially varying aspects of the object structures for further studies. The result obtained with the experiments shows significant improvement in classifying and clustering small and large surface structures of topographic reliefs over other known methods. It establishes the relevance of using spatial clustering and the use of surface normals for clustering object structures of varying sizes on the earth's surface.

当表面返回时，点云包含大量的点数据，以描述地形特征的多维方面。这些点云中原子级别的数据点以原始形式传达的信息很少或没有传达有关场景内感兴趣对象的结构，物理和空间信息。将表面描述为一组点是复杂且具有挑战性的。本文介绍了使用空间聚类和点云数据聚合来分析地形浮雕和地形区域以提取表面特征的框架和方法。该方法还描述了Kd- 树和最近邻估计，以分割和可视化表面地形地形和起伏区域。证明了簇中的点与相应表面之间的形态对应关系。它也被定义为对象结构的簇和空间变化方面，以供进一步研究。通过实验获得的结果表明，与其他已知方法相比，在对地形浮雕的大小表面结构进行分类和聚类方面有显着改进。它建立了使用空间聚类和使用表面法线对地球表面上大小不同的对象结构进行聚类的相关性。