Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform


KOÇ SAN D., SELİM S., ASLAN N., SAN B. T.

COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol.150, pp.289-301, 2018 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 150
  • Publication Date: 2018
  • Doi Number: 10.1016/j.compag.2018.05.001
  • Journal Name: COMPUTERS AND ELECTRONICS IN AGRICULTURE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.289-301
  • Keywords: Citrus tree, UAV, Circular Hough transform, Tree extraction, CROWN DELINEATION, SPECIES CLASSIFICATION, FOREST INVENTORY, BUILDING EXTRACTION, URBAN AREAS, LIDAR DATA, ACCURACY, SEGMENTATION, PARAMETERS, ALGORITHM
  • Akdeniz University Affiliated: Yes

Abstract

Tree counts and sizes are important information to apply to crop yield estimation and agricultural planning. Therefore, obtaining automatic extraction of trees, their locations, diameters, and counts from remotely sensed data is a challenging task. In this study, a novel approach is proposed for the automatic extraction of citrus trees using unmanned aerial vehicle (UAV) multispectral images (MSIs) and digital surface models (DSMs). The tree boundaries were extracted by using sequential thresholding, Canny edge detection and circular Hough transform algorithms. The performance of the developed approach was assessed on three test areas that include different characteristics with regard to tree counts, diameters, densities and background covers. The proposed tree extraction procedure was applied to DSM that were generated from UAV images (Data Set 1), UAV MSIs (Data Set 2) and both of them together (Data Set 3). The accuracies of the obtained results were assessed using three different techniques that evaluate the tree extraction results according to the counts, areas and locations. The obtained results indicate the success of the developed approach with delineation accuracies that exceeded 80% for each test area using each data set. The most accurate results were obtained when Data Set 1 was used. Although Data Set 2 provides the lowest accuracies when compared with other data sets, the delineation accuracies are still high and can be used especially for counting trees and detecting tree locations.