Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 415-420, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-415-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 415-420, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-415-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  25 Sep 2013

25 Sep 2013

DETECTION OF TREE CROWNS BASED ON RECLASSIFICATION USING AERIAL IMAGES AND LIDAR DATA

S. Talebi1, A. Zarea2, S. Sadeghian3, and H. Arefi4 S. Talebi et al.
  • 1Geomatics Engineering Faculty, Tafresh State University, Tafresh, Iran
  • 2Geodesy and Geomatics Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran
  • 3Geomatics college of National Cartographic Center (NCC), Tehran, Iran
  • 4Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234, Wessling, Germany

Keywords: nDSM, Shadow Index, NDVI, Unsupervised, Hierarchal, LiDAR, Reclassification, Tree Detection

Abstract. Tree detection using aerial sensors in early decades was focused by many researchers in different fields including Remote Sensing and Photogrammetry. This paper is intended to detect trees in complex city areas using aerial imagery and laser scanning data. Our methodology is a hierarchal unsupervised method consists of some primitive operations. This method could be divided into three sections, in which, first section uses aerial imagery and both second and third sections use laser scanners data. In the first section a vegetation cover mask is created in both sunny and shadowed areas. In the second section Rate of Slope Change (RSC) is used to eliminate grasses. In the third section a Digital Terrain Model (DTM) is obtained from LiDAR data. By using DTM and Digital Surface Model (DSM) we would get to Normalized Digital Surface Model (nDSM). Then objects which are lower than a specific height are eliminated. Now there are three result layers from three sections. At the end multiplication operation is used to get final result layer. This layer will be smoothed by morphological operations. The result layer is sent to WG III/4 to evaluate. The evaluation result shows that our method has a good rank in comparing to other participants’ methods in ISPRS WG III/4, when assessed in terms of 5 indices including area base completeness, area base correctness, object base completeness, object base correctness and boundary RMS. With regarding of being unsupervised and automatic, this method is improvable and could be integrate with other methods to get best results.