When a populated area is inundated, the availability of a flood extent map becomes vital to assist the local authorities to plan rescue operations and evacuate the premises promptly. This paper proposes a novel automatic way to rapidly map the flood extent using a supervised classifier. The methodology described in this paper is fully automated since the training of the supervised classifier is made starting from water and land masks derived from the Normalized Difference Water Index (NDWI), and without any intervention from the human operator. Both a pre-event Synthetic Aperture Radar (SAR) image and an optical Sentinel-2 image are needed to train the supervised classifier to identify the inundation on the flooded SAR image. The entire flood mapping process, which consists of preprocessing the images, the extraction of the training dataset, and finally the classification, was assessed on flood events which occurred in Tewkesbury (England) in 2007 and in Myanmar in 2015, and were captured by TerraSAR-X and Sentinel-1, respectively. This algorithm was found to offer overall a good compromise between computation time and precision of the classification, making it suitable for emergency situations. In fact, the inundation maps produced for the previous two flood events were in agreement with the ground truths for over 90% of the pixels in the SAR images. Besides, the latter process took less than 5 min to finish the flood mapping from a SAR image of more than 41 million pixels for the dataset capturing the flood in Tewkesbury, and around 2 min and 40 s for an image of 19 million pixels of the flood in Myanmar.
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