Performance comparison of different inpainting algorithms for accurate DTM generation

To accurately extract digital terrain model (DTM), it is necessary to remove heights due to vegetation such as trees and shrubs and other manmade structures such as buildings, bridges, etc. from the digital surface model (DSM). The resulting DTM can then be used for construction planning, land surveying, etc. Normally, the process of extracting DTM involves two steps. First, accurate land cover classification is required. Second, an image inpainting process is needed to fill in the missing pixels due to trees, buildings, bridges, etc. In this paper, we focus on the second step of using image inpainting algorithms for terrain reconstruction. In particular, we evaluate seven conventional and deep learning based inpainting algorithms in the literature using two datasets. Both objective and subjective comparisons were carried out. It was observed that some algorithms yielded slightly better performance than others.