Enhancement of detecting permanent water and temporary water in flood disasters by fusing sentinel-1 and sentinel-2 imagery using deep learning algorithms: Demonstration of sen1floods11 benchmark datasets

Yanbing Bai, Wenqi Wu, Zhengxin Yang, Jinze Yu, Bo Zhao, Xing Liu, Hanfang Yang, Erick Mas, Shunichi Koshimura

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multisource data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

Original languageEnglish
Article number2220
Pages (from-to)NA
JournalRemote Sensing
Volume13
Issue number11
DOIs
Publication statusPublished - 2021 Jun 1

Keywords

  • Deep learning
  • Flood
  • Multi-source data fusion
  • Permanent water
  • Sen1Floods11 datasets
  • Sentinel-1
  • Sentinel-2
  • Temporary water

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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