Multidimensional spatial clustering and visualization of 3D topographic relief data

Abstract

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.