Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 985-990, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/985/2016/
doi:10.5194/isprs-archives-XLI-B1-985-2016
 
06 Jun 2016
CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS
Ravi Ancil Persad and Costas Armenakis Geomatics Engineering, GeoICT Lab Earth and Space Science and Engineering Lassonde School of Engineering, York University 4700 Keele St., Toronto, Ontario, M3J 1P3 Canada
Keywords: Co-registration, DSM, Automation, Wavelets, Matching, UAV, Laser Scanning Abstract. An approach for the co-registration of Digital Surface Models (DSMs) derived from Unmanned Aerial Vehicles (UAVs) and Terrestrial Laser Scanners (TLS) is proposed. Specifically, a wavelet-based feature descriptor for matching surface keypoints on the 2.5D DSMs is developed. DSMs are useful in wide-scope of various applications such as 3D building modelling and reconstruction, cultural heritage, urban and environmental planning, aircraft navigation/path routing, accident and crime scene reconstruction, mining as well as, topographic map revision and change detection. For these listed applications, it is not uncommon that there will be a need for automatically aligning multi-temporal DSMs which may have been acquired from multiple sensors, with different specifications over a period of time, and may have various overlaps. Terrestrial laser scanners usually capture urban facades in an accurate manner; however this is not the case for building roof structures. On the other hand, vertical photography from UAVs can capture the roofs. Therefore, the automatic fusion of UAV and laser-scanning based DSMs is addressed here as it serves various geospatial applications.
Conference paper (PDF, 1826 KB)


Citation: Persad, R. A. and Armenakis, C.: CO-REGISTRATION OF DSMs GENERATED BY UAV AND TERRESTRIAL LASER SCANNING SYSTEMS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 985-990, doi:10.5194/isprs-archives-XLI-B1-985-2016, 2016.

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