Enhancing LiDAR-UAS Derived Digital Terrain Models with Hierarchic Robust and Volume-Based Filtering Approaches for Precision Topographic Mapping

Author:

Oniga Valeria-Ersilia1ORCID,Loghin Ana-Maria2ORCID,Macovei Mihaela1,Lazar Anca-Alina1,Boroianu Bogdan3,Sestras Paul45ORCID

Affiliation:

1. Department of Terrestrial Measurements and Cadastre, Faculty of Hydrotechnical Engineering, Geodesy and Environmental Engineering, “Gheorghe Asachi” Technical University of Iasi, Professor Dimitrie Mangeron Boulevard 67, 700050 Iași, Romania

2. Department of Remote Sensing, Federal Office of Metrology and Surveying, Schiffamtsgasse 1-3, 1020 Vienna, Austria

3. Geocad Profesional SRL (Smart Imaging and Mapping Services-SIMS), 022683 București, Romania

4. Department of Land Measurements and Cadastre, Faculty of Civil Engineering, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania

5. Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania

Abstract

Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With its high point density and the vast majority of points (approximately 99%) measured with the first return, filtering LiDAR-UAS data proves to be a more challenging task when compared to ALS point clouds. Various algorithms have been proposed in the scientific literature to differentiate ground points from non-ground points. Each of these algorithms has advantages and disadvantages, depending on the specific terrain characteristics. The aim of this research is to obtain an enhanced Digital Terrain Model (DTM) based on LiDAR-UAS data and to qualitatively and quantitatively compare three filtering approaches, i.e., hierarchical robust, volume-based, and cloth simulation, on a complex terrain study area. For this purpose, two flights over a residential area of about 7.2 ha were taken at 60 m and 100 m, with a DJI Matrice 300 RTK UAS, equipped with a Geosun GS-130X LiDAR sensor. The vertical and horizontal accuracy of the LiDAR-UAS point cloud, obtained via PPK trajectory processing, was tested using Check Points (ChPs) and manually extracted features. A combined approach for ground point classification is proposed, using the results from a hierarchic robust filter and applying an 80% slope condition for the volume-based filtering result. The proposed method has the advantage of representing with accuracy man-made structures and sudden slope changes, improving the overall accuracy of the DTMs by 40% with respect to the hierarchical robust filtering algorithm in the case of a 60 m flight height and by 28% in the case of a 100 m flight height when validated against 985 ChPs.

Funder

Ministry of Research, Innovation and Digitization, CNCS-UEFISCDI

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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