Volume XL-1/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W3, 153-158, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-153-2013
© 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, 153-158, 2013
https://doi.org/10.5194/isprsarchives-XL-1-W3-153-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Sep 2013

24 Sep 2013

TREE CROWN DELINEATION ON VHR AERIAL IMAGERY WITH SVM CLASSIFICATION TECHNIQUE OPTIMIZED BY TAGUCHI METHOD: A CASE STUDY IN ZAGROS WOODLANDS

Y. Erfanifard1, N. Behnia1, and V. Moosavi2 Y. Erfanifard et al.
  • 1Dept. of Desert Management, College of Agriculture, Shiraz University, Shiraz, Iran
  • 2Dept. of Watershed Management Engineering, Faculty of Natural Resources, Yazd University, Yazd, Iran

Keywords: Aerial imagery, Classification, SVM, Taguchi method, Zagros, Woodland

Abstract. The Support Vector Machine (SVM) is a theoretically superior machine learning methodology with great results in classification of remotely sensed datasets. Determination of optimal parameters applied in SVM is still vague to some scientists. In this research, it is suggested to use the Taguchi method to optimize these parameters. The objective of this study was to detect tree crowns on very high resolution (VHR) aerial imagery in Zagros woodlands by SVM optimized by Taguchi method. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The VHR aerial imagery of the plot with 0.06 m spatial resolution was obtained from National Geographic Organization (NGO), Iran, to extract the crowns of Persian oak trees in this study. The SVM parameters were optimized by Taguchi method and thereafter, the imagery was classified by the SVM with optimal parameters. The results showed that the Taguchi method is a very useful approach to optimize the combination of parameters of SVM. It was also concluded that the SVM method could detect the tree crowns with a KHAT coefficient of 0.961 which showed a great agreement with the observed samples and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimize the parameters of classification techniques like SVM.