An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring

With the advent of unmanned aerial vehicle (UAV)-based photogrammetry and structure from motion (SFM) software, it is possible to obtain high-density point clouds of which the accuracy can meet the requirements of river bank monitoring. Ground filtering, i.e., removing the points belonging to above-ground objects, is an important process of digital terrain model (DTM) generation which is essential to river bank monitoring. Progressive morphological filter (PM) is a widely-adopted ground filtering algorithm and performs well with LiDAR data. However, it may incorrectly classify vegetation points as ground points when used to filter UAV-based photogrammetric point clouds because ground points beneath vegetation cannot be captured with the digital camera on-board UAV. In this study, we propose the improved progressive morphological filter (IPM) algorithm to improve the accuracy of ground filtering on UAV-based photogrammetric point clouds by introducing visible-band difference vegetation index (VDVI) to PM. The proposed IPM is subsequently evaluated along with the original PM algorithm and four other widely-used ground filtering algorithms in four test sites along the Yangtze River. The results show that IPM improves the overall accuracy from PM in all the four test sites, and produces the best results among the six ground filtering algorithms in three out of the four sites. IPM proves to be an effective ground filtering algorithm for UAV-based photogrammetric point clouds in river bank monitoring.