Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

Abstract

Developing algorithms for generating accurate Digital Terrain Model (DTM) of rivers is necessary due to the limitations of traditional field survey methods, which are time-consuming and costly and do not provide continuous data. The objective of this study was to develop an advanced algorithm for generating high-quality DTM of rivers using Structur from Motion (SfM) data. A leveling survey was conducted on four cross-sections of the Bokha stream in Icheon City, S. Korea, and SfM-based DTM was produced using the Pix4Dmapper program and Phantom 4 multispectral drone. Two vegetation filters (NDVI and ExG) and two morphological filters (ATIN and CSF) were applied to the data, and the best filter combination was identified based on MAE and RMSE analyses. The integration of NDVI and CSF showed the best performance for the vegetation area, while a single application of NDVI showed the lowest MAE for the bare area. The effectiveness of the SfM method in eliminating waterfront vegetation was confirmed, with an overall MAE of 0.299 m RMSE of 0.375 m. These findings suggest that generating DTMs of riparian zones can be achieved efficiently with a limited budget and time using the proposed methodology.