Next Article in Journal
Toward Super-Resolution Image Construction Based on Joint Tensor Decomposition
Next Article in Special Issue
Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation
Previous Article in Journal
Monitoring Pasture Aboveground Biomass and Canopy Height in an Integrated Crop–Livestock System Using Textural Information from PlanetScope Imagery
Previous Article in Special Issue
Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation
 
 
Article

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

1
United Nations Institute for Training and Research’s (UNITAR) Operational Satellite Applications Programme (UNOSAT), CERN, 1211 Meyrin, Switzerland
2
United Nations Global Pulse, New York, NY 10017, USA
3
Institute for Data Science, Durham University, Durham DH1 3LE, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(16), 2532; https://doi.org/10.3390/rs12162532
Received: 1 June 2020 / Revised: 29 July 2020 / Accepted: 30 July 2020 / Published: 6 August 2020
(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)
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. View Full-Text
Keywords: microwave remote sensing; rapid mapping; disaster response; flood mapping; image segmentation; machine learning; convolutional neural network microwave remote sensing; rapid mapping; disaster response; flood mapping; image segmentation; machine learning; convolutional neural network
Show Figures

Graphical abstract

MDPI and ACS Style

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

AMA Style

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

Chicago/Turabian Style

Nemni, Edoardo, Joseph Bullock, Samir Belabbes, and Lars Bromley. 2020. "Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery" Remote Sensing 12, no. 16: 2532. https://doi.org/10.3390/rs12162532

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop