The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 413–418, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-413-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 413–418, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-413-2019

  04 Jun 2019

04 Jun 2019

UAV AND LIDAR IMAGE REGISTRATION: A SURF-BASED APPROACH FOR GROUND CONTROL POINTS SELECTION

B. Kalantar1, N. Ueda1, H. A. H. Al-Najjar2, H. Moayedi3,4, A. A. Halin5, and S. Mansor6 B. Kalantar et al.
  • 1RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
  • 2Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, 2007 NSW, Australia
  • 3Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  • 4Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  • 5Dept. of Multimedia, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
  • 6Dept. of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia

Keywords: Image registration, UAV images, LiDAR, SURF, Feature based

Abstract. Multisource remote sensing image data provides synthesized information to support many applications including land cover mapping, urban planning, water resource management, and GIS modelling. Effectively utilizing such images however requires proper image registration, which in turn highly relies on accurate ground control points (GCP) selection. This study evaluates the performance of the interest point descriptor SURF (Speeded-Up Robust Features) for GCPs selection from UAV and LiDAR images. The main motivation for using SURF is due to it being invariant to scaling, blur and illumination, and partially invariant to rotation and view point changes. We also consider features generated by the Sobel and Canny edge detectors as complements to potentially increase the accuracy of feature matching between the UAV and LiDAR images. From our experiments, the red channel (Band-3) produces the most accurate and practical results in terms of registration, while adding the edge features seems to produce lacklustre results.