Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery

Jamali, Ali; Roy, Swalpa Kumar; Hashemi Beni, Leila; Pradhan, Biswajeet; Li, Jonathan; Ghamisi, Pedram

doi:10.1016/j.jag.2024.103662

Cited by 6 publications

(1 citation statement)

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“…The results show that the transformer combined with the CNN yields better results than the singular models. The authors of [13] compare several segmentation models (WVResU-Net, Swin U-Net, U-Net+++, Attention U-Net, R2U-Net, ResU-Net, TransU-Net, and TransU-Net++) to successfully map flooded areas using Sentinel-1 SAR images. Similarly, in [14], SAR images from Sentinel-1 are used to map inundation extents of lakes.…”

Section: Introductionmentioning

confidence: 99%

Vision Transformer for Flood Detection Using Satellite Images from Sentinel-1 and Sentinel-2

Chamatidis,

Istrati,

Lagaros

2024

Water

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Floods are devastating phenomena that occur almost all around the world and are responsible for significant losses, in terms of both human lives and economic damages. When floods occur, one of the challenges that emergency response agencies face is the identification of the flooded area so that access points and safe routes can be determined quickly. This study presents a flood detection methodology that combines transfer learning with vision transformers and satellite images from open datasets. Transformers are powerful models that have been successfully applied in Natural Language Processing (NLP). A variation of this model is the vision transformer (ViT), which can be applied to image classification tasks. The methodology is applied and evaluated for two types of satellite images: Synthetic Aperture Radar (SAR) images from Sentinel-1 and Multispectral Instrument (MSI) images from Sentinel-2. By using a pre-trained vision transformer and transfer learning, the model is fine-tuned on these two datasets to train the models to determine whether the images contain floods. It is found that the proposed methodology achieves an accuracy of 84.84% on the Sentinel-1 dataset and 83.14% on the Sentinel-2 dataset, revealing its insensitivity to the image type and applicability to a wide range of available visual data for flood detection. Moreover, this study shows that the proposed approach outperforms state-of-the-art CNN models by up to 15% on the SAR images and 9% on the MSI images. Overall, it is shown that the combination of transfer learning, vision transformers, and satellite images is a promising tool for flood risk management experts and emergency response agencies.

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Section: Introductionmentioning

confidence: 99%