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http://dx.doi.org/10.7780/kjrs.2020.36.5.3.8

Extracting Flooded Areas in Southeast Asia Using SegNet and U-Net  

Kim, Junwoo (School of Earth and Environmental Sciences, Seoul National University)
Jeon, Hyungyun (School of Earth and Environmental Sciences, Seoul National University)
Kim, Duk-jin (School of Earth and Environmental Sciences, Seoul National University)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_3, 2020 , pp. 1095-1107 More about this Journal
Abstract
Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.
Keywords
SegNet; U-Net; Sentinel-1 A/B; Flooded area extraction; Deep Learning; Semantic Segmentation;
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