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Open AccessArticle

An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

1
School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing 210044, China
2
Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN Delft, The Netherlands
3
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 647; https://doi.org/10.3390/rs11060647
Received: 26 November 2018 / Revised: 25 February 2019 / Accepted: 1 March 2019 / Published: 16 March 2019
(This article belongs to the Special Issue Landslide Hazard and Risk Assessment)
Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs. View Full-Text
Keywords: deformation detection; terrestrial laser scanning; multi-temporal; pairwise registration; multi-station adjustment; hillside areas deformation detection; terrestrial laser scanning; multi-temporal; pairwise registration; multi-station adjustment; hillside areas
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Zang, Y.; Yang, B.; Li, J.; Guan, H. An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas. Remote Sens. 2019, 11, 647.

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