An optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data

  • Ahmed Abdulkareem Ahmed
  • Biswajeet PradhanEmail author
  • Maher Ibrahim Sameen
  • Ali Muayad Makky
GCEC 2017
Part of the following topical collections:
  1. Global Sustainability through Geosciences and Civil Engineering


This study proposed a workflow for an optimized object-based analysis for vegetation mapping using integration of Quickbird and Sentinel-1 data. The method is validated on a set of data captured over a part of Selangor located in the Peninsular Malaysia. The method comprised four components including image segmentation, Taguchi optimization, attribute selection using random forest, and rule-based feature extraction. Results indicated the robustness of the proposed approach as the area under curve of forest; grassland, old oil palm, rubber, urban tree, and young oil palm were calculated as 0.90, 0.89, 0.87, 0.87, 0.80, and 0.77, respectively. In addition, results showed that SAR data is very useful for extracting rubber and young oil palm trees (given by random forest importance values). Finally, further research is suggested to improve segmentation results and extract more features from the scene.


Vegetation mapping Taguchi optimization Random forest OBIA Remote sensing GIS 


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Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • Ahmed Abdulkareem Ahmed
    • 1
  • Biswajeet Pradhan
    • 1
    Email author
  • Maher Ibrahim Sameen
    • 1
  • Ali Muayad Makky
    • 1
  1. 1.School of Systems, Management and Leadership, Faculty of Engineering and Information TechnologyUniversity of Technology SydneyUltimoAustralia

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