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http://dx.doi.org/10.7780/kjrs.2022.38.4.5

Performance of Random Forest Classifier for Flood Mapping Using Sentinel-1 SAR Images  

Chu, Yongjae (Department of Geophysics, Kangwon National University)
Lee, Hoonyol (Department of Geophysics, Kangwon National University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.4, 2022 , pp. 375-386 More about this Journal
Abstract
The city of Khartoum, the capital of Sudan, was heavily damaged by the flood of the Nile in 2020. Classification using satellite images can define the damaged area and help emergency response. As Synthetic Aperture Radar (SAR) uses microwave that can penetrate cloud, it is suitable to use in the flood study. In this study, Random Forest classifier, one of the supervised classification algorithms, was applied to the flood event in Khartoum with various sizes of the training dataset and number of images using Sentinel-1 SAR. To create a training dataset, we used unsupervised classification and visual inspection. Firstly, Random Forest was performed by reducing the size of each class of the training dataset, but no notable difference was found. Next, we performed Random Forest with various number of images. Accuracy became better as the number of images in creased, but converged to a maximum value when the dataset covers the duration from flood to the completion of drainage.
Keywords
Classification; Random forest; Flood; Disaster; SAR; Sentinel-1;
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Times Cited By KSCI : 3  (Citation Analysis)
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