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

Bayram B., Şeker D. Z., Jamil A., Reis H. C., DEMİR N., Bozkurt S., ...More

ARABIAN JOURNAL OF GEOSCIENCES, vol.11, no.12, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 12
  • Publication Date: 2018
  • Doi Number: 10.1007/s12517-018-3680-6
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Yıldız Technical University Affiliated: Yes


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.