Comprehensive comparison of two image-based point clouds from aerial photos with airborne lidar for large-scale mapping

Abstract

The
integration of computer vision and photogrammetry to generate three-dimensional
(3D) information from images has contributed to a wider use of point clouds,
for mapping purposes. Large-scale topographic map production requires 3D data
with high precision and accuracy to represent the real conditions of the earth
surface. Apart from LiDAR point clouds, the image-based matching is also
believed to have the ability to generate reliable and detailed point clouds
from multiple-view images. In order to examine and analyze possible fusion of
LiDAR and image-based matching for large-scale detailed mapping purposes, point
clouds are generated by Semi Global Matching (SGM) and by Structure from Motion
(SfM). In order to conduct comprehensive and fair comparison, this study uses
aerial photos and LiDAR data that were acquired at the same time. Qualitative
and quantitative assessments have been applied to evaluate LiDAR and
image-matching point clouds data in terms of visualization, geometric accuracy,
and classification result. The comparison results conclude that LiDAR is the
best data for large-scale mapping.