Mapping hazelnut trees from high resolution digital orthophoto maps: A quantitative comparison of an object and a pixel based approaches

Akhtar Jamil*, Bulent Bayram, Dursun Zafer Seker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This study investigates the suitability of object- and pixel-based approaches for extraction of hazelnut trees from high resolution digital orthophoto maps. For object-based approach, simple linear iterative clustering (SLIC) method was employed to segment image pixels into homogeneous regions. Features spanning spectral, spatial and textural domains were extracted from each segment then classification was performed by employing support vector machine (SVM) classifier. For pixel-based approach, the spectral reflectance information from all four bands were used as features and applied maximum likelihood (ML) classifier for classification of each pixel into hazelnut and other tree species classes. An area-based approach was used to evaluate the performance of the proposed method. The experiments showed that overall classification accuracy for object-based method was superior to the pixel-based method. Using object-based approach the overall accuracy obtained was 86% while pixel-based approach scored 76%.

Original languageEnglish
Pages (from-to)561-567
Number of pages7
JournalFresenius Environmental Bulletin
Volume28
Issue number2
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 Parlar Scientific Publications. All rights reserved.

Funding

The authors would like to thank EMI Group Inc., Turkey for providing aerial imagery and reference data. This study was part of a project funded by TUBITAK under grant no. 7140512. Theprojectwas supervised by EMI Group-Turkey, and consulted by Prof.Dr.B.Bayram.

FundersFunder number
EMI Group Inc.
TUBITAK7140512

    Keywords

    • Maximum likelihood
    • Simple linear iterative clustering
    • Support vector machine
    • Tree species classification

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