Volume IV-2/W3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W3, 9-16, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W3, 9-16, 2017
https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Aug 2017

18 Aug 2017

SLIC SUPERPIXELS FOR OBJECT DELINEATION FROM UAV DATA

S. Crommelinck1, R. Bennett2, M. Gerke3, M. N. Koeva1, M. Y. Yang1, and G. Vosselman1 S. Crommelinck et al.
  • 1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands
  • 2Faculty of Business and Law, Swinburne University of Technology, Victoria, Australia
  • 3Institute of Geodesy und Photogrammetry, Technical University of Brunswick, Brunswick, Germany

Keywords: UAV Photogrammetry, Image Segmentation, Object Detection, Contour Detection, Image Analysis, Land Administration, Cadastral Boundaries, Cadastral Mapping

Abstract. Unmanned aerial vehicles (UAV) are increasingly investigated with regard to their potential to create and update (cadastral) maps. UAVs provide a flexible and low-cost platform for high-resolution data, from which object outlines can be accurately delineated. This delineation could be automated with image analysis methods to improve existing mapping procedures that are cost, time and labor intensive and of little reproducibility. This study investigates a superpixel approach, namely simple linear iterative clustering (SLIC), in terms of its applicability to UAV data. The approach is investigated in terms of its applicability to high-resolution UAV orthoimages and in terms of its ability to delineate object outlines of roads and roofs. Results show that the approach is applicable to UAV orthoimages of 0.05 m GSD and extents of 100 million and 400 million pixels. Further, the approach delineates the objects with the high accuracy provided by the UAV orthoimages at completeness rates of up to 64 %. The approach is not suitable as a standalone approach for object delineation. However, it shows high potential for a combination with further methods that delineate objects at higher correctness rates in exchange of a lower localization quality. This study provides a basis for future work that will focus on the incorporation of multiple methods for an interactive, comprehensive and accurate object delineation from UAV data. This aims to support numerous application fields such as topographic and cadastral mapping.