Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery

Nemni, Edoardo; Bullock, Joseph; Belabbes, Samir; Bromley, Lars

Abstract

Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring.

Citation

Nemni, E., Bullock, J., Belabbes, S., & Bromley, L. (2020). Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery. Remote Sensing, 12(16), Article 2532. https://doi.org/10.3390/rs12162532

• Journal Article Type: Article
• Acceptance Date: Jul 30, 2020
• Online Publication Date: Aug 6, 2020
• Publication Date: 2020-08
• Deposit Date: Aug 11, 2020
• Publicly Available Date: Aug 11, 2020
• Journal: Remote Sensing
• Electronic ISSN: 2072-4292
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 16
• Article Number: 2532
• DOI: https://doi.org/10.3390/rs12162532
• Public URL: https://durham-repository.worktribe.com/output/1263837