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

Publication Type:
Journal Article
Citation:
Arabian Journal of Geosciences, 2018, 11 (11)
Issue Date:
2018-06-01
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
Ahmed2018_Article_AnOptimizedObject-basedAnalysi.pdfPublished Version7.23 MB
Adobe PDF
© 2018, Saudi Society for Geosciences. 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.
Please use this identifier to cite or link to this item: