Journal cover Journal topic
Proceedings of the ICA
Journal topic
Volume 1
Proc. Int. Cartogr. Assoc., 1, 56, 2018
https://doi.org/10.5194/ica-proc-1-56-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Proc. Int. Cartogr. Assoc., 1, 56, 2018
https://doi.org/10.5194/ica-proc-1-56-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 May 2018

16 May 2018

An object-based approach for tree species extraction from digital orthophoto maps

Akhtar Jamil and Bulent Bayram Akhtar Jamil and Bulent Bayram
  • Yildiz Technical University, Department of Geomatic Engineering, Istanbul, Turkey

Keywords: Tree segmentation, mean shift, hazelnut tree classification, decision trees, normalized difference vegetation index, morphological processing

Abstract. Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1:5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85% for all sample images.

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