Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

Adam Salach,Krzysztof Bakuła,Konrad Górski,Wojciech Ostrowski,Magdalena Pilarska,Zdzisław Kurczyński

doi:10.3390/ijgi7090342

Abstract

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Highlights

The subject of accuracy comparison of two leading airborne data sources, airborne laser scanning (ALS) and photogrammetric image-matching, has been discussed in several References [1,2,3]
The presented analysis of such datasets has usually indicated that Unmanned Aerial Vehicles (UAVs) Laser Scanning (ULS) technology is more accurate than Dense Image Matching (DIM), according to the vertical accuracy analysis in areas covered by vegetation
The DTM from DIM point cloud (DTMDIM) and DTMULS were evaluated based on the GNSS RTK measurement and compared with the airborne laser scanning

Summary

Introduction

The subject of accuracy comparison of two leading airborne data sources, airborne laser scanning (ALS) and photogrammetric image-matching, has been discussed in several References [1,2,3]. Such a discussion is present in a comparison of close-range datasets, i.e., terrestrial laser scanning data was compared with the Structure-from-Motion technique in many applications [4,5,6,7]. The largest disadvantage of point clouds achieved from photogrammetry is the vegetation influence, decreasing the vertical accuracy of the derived digital terrain model [16,17]. In cases where the DTM is not significantly important and the only product to assess is the Digital Surface Model (DSM), UAV photogrammetry can provide accuracies better than 0.10 m [15,23,24,25], even with compact cameras

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Discussion

Conclusion