Volume XLII-2/W7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 557-565, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W7, 557-565, 2017
https://doi.org/10.5194/isprs-archives-XLII-2-W7-557-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2017

12 Sep 2017

COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING

E. Widyaningrum1,2 and B. G. H. Gorte1 E. Widyaningrum and B. G. H. Gorte
  • 1Dept. of Geoscience and Remote Sensing, Civil Engineering and Geosciences Faculty, Technical University of Delft, the Netherlands
  • 2Centre for Topographic Base Mapping and Toponyms, Geospatial Information Agency, Bogor, Indonesia

Keywords: Point Clouds, Aerial Photos, LiDAR, Semi Global Matching, Structure from Motion, Comparison Analyses

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.