Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform
Autor: | Bekir Taner San, Dilek Koc-San, Serdar Selim, Nagihan Aslan |
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Rok vydání: | 2018 |
Předmět: |
010504 meteorology & atmospheric sciences
Computer science business.industry Multispectral image 0211 other engineering and technologies Forestry Pattern recognition 02 engineering and technology Horticulture 01 natural sciences Thresholding Computer Science Applications Hough transform law.invention Data set Tree (data structure) law Canny edge detector Extraction (military) Artificial intelligence Digital surface business Agronomy and Crop Science 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
Zdroj: | Computers and Electronics in Agriculture. 150:289-301 |
ISSN: | 0168-1699 |
DOI: | 10.1016/j.compag.2018.05.001 |
Popis: | 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. |
Databáze: | OpenAIRE |
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