Automatic extraction of sparse trees from high-resolution ortho-images

  • Bulent Bayram
  • Dursun Zafer Seker
  • Akhtar Jamil
  • Hatice Catal Reis
  • Nusret DemirEmail author
  • Salih Bozkurt
  • Abdulkadir Ince
  • Turgay Kucuk
Original Paper


Obtaining information about tree species distribution in agricultural lands is a topic of interest for various applications, such as tree inventory, forest management, agricultural land management, crop estimation, etc. This information can be derived from images obtained from modern remote sensing technology, which is the most economical way as compare to field surveys covering large geographic areas. Therefore, in this study, a new method is proposed for extraction and counting of sparse and regular distributed individual pistachio trees from agricultural areas on large scale from high-resolution digital ortho-photo maps, which were obtained using an airborne sensor (Ultracam-X). The input images were first smoothed by applying Gaussian filter to reduce the impact of noise. Normalized difference vegetation indices (NDVI) were then derived to obtain vegetation areas followed by Otsu’s global thresholding algorithm to obtain candidate tree areas. Further, connected component (CC) analysis was applied to segregate each object. Morphological processing was performed to fill holes within tree objects and get smooth contours, which were obtained by using the Moore-neighbor tracing method (MNTM) for each CC, while geometrical constraints were applied to undermine possible non-tree elements from output image. To further improve the segmentation results for sparse trees, a new method was applied, called quadratic local analysis (QLA). QLA helped to segment the trees, which were missed by the Otsu method due to low contrast and resulted in improved accuracy (3–6%). The obtained results were compared with well-known support vector machine (SVM) classifier. Proposed method produced slightly better results (1–5%) than SVM for extraction of pistachio trees and obtained accuracy for QLA and SVM were 96 and 91% for region 1, while 91 and 90% for region 2 respectively.


Automatic digitization Sparse tree extraction Otsu method Moore-neighbor tracing method Connect component analysis Morphological processing Support vector machine 


Funding information

This study is a part of TEYDEP Project entitled “Development of Object Based Neural Network Image Processing System Determination of Vegetation and Forestry Boundaries” (Project Nr. 7140512). It was supervised by EMI Group-Turkey and consulted by Prof. Dr. Bulent Bayram.


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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Bulent Bayram
    • 1
  • Dursun Zafer Seker
    • 2
  • Akhtar Jamil
    • 1
  • Hatice Catal Reis
    • 3
  • Nusret Demir
    • 4
    Email author
  • Salih Bozkurt
    • 1
  • Abdulkadir Ince
    • 1
  • Turgay Kucuk
    • 5
  1. 1.Department of Geomatics EngineeringYTU, Yildiz Technical UniversityIstanbulTurkey
  2. 2.Department of Geomatics EngineeringITU, Istanbul Technical UniversityIstanbulTurkey
  3. 3.Faculty of Engineering and Natural Sciences, Department of Geomatics EngineeringGumushane UniversityGumushaneTurkey
  4. 4.Faculty of Science, Department of Space Science and Technologies, Remote Sensing Application and Research CenterAkdeniz UniversityAntalyaTurkey
  5. 5.EMI Group Information Technologies Incorporated CompanyIstanbulTurkey

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