Projection-Based Spatial Morphology for Extracting Ridge and Valley Profiles of Mountains from 3D Amorphous Data

Terrain modeling and visualization requires estimating geometrical limits and shape profiles of its surface. The task becomes challenging for multidimensional amorphous data that are organized as point clouds. In this paper, we describe a simple, yet effective way of estimating ridge and valley profiles from a given point cloud data of a mountainous region. We have proposed a projection-based spatial morphological extraction framework for detecting mountain profile from amorphous data and also demonstrate optimizing criteria that uses spatial cohesion between the space points and directional attributes of the surface composition to arrive at the results. It is independent of the mode of data acquisition and requires only point data set with positional and elevational attributes associated with each point. The proposed methods can be controlled for thickness and spatial continuity for a better approximation of the surface structure. Our method accounts for missing data values and interpolates them for better results. The results are visually analyzed and results maintain a high degree of visual accuracy.