Article
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Flood Segmentation: Self-Supervised Knowledge Transfer from Optical to SAR
Version 1
: Received: 21 June 2024 / Approved: 21 June 2024 / Online: 21 June 2024 (14:45:00 CEST)
How to cite: Pignato, S.; Marino, A. Flood Segmentation: Self-Supervised Knowledge Transfer from Optical to SAR. Preprints 2024, 2024061541. https://doi.org/10.20944/preprints202406.1541.v1 Pignato, S.; Marino, A. Flood Segmentation: Self-Supervised Knowledge Transfer from Optical to SAR. Preprints 2024, 2024061541. https://doi.org/10.20944/preprints202406.1541.v1
Abstract
Our world is increasingly challenged by managing the impacts of natural disasters, particularly floods, which are frequent, dangerous, and costly. Traditional flood mapping methods, reliant on Optical satellites (MSI), struggle under cloud cover which is typical during such events. Synthetic Aperture Radar (SAR) offers a promising alternative with its cloud-penetrating capability, though its use has been limited due to complexity and data labeling challenges. This project aims to develop a SAR-based flood segmentation model that can rapidly and accurately map floods globally, with high adaptability to different flood types and regions. By utilizing deep learning and a novel transfer-learning technique that combines the strengths of Optical/MSI and SAR data, the model seeks to bypass the challenges of manual labeling and improve mapping accuracy. Initial results show the model's effective generalization across various flood events, with superior performance indicated by an Intersection over Union (IoU) of 0.72, outperforming existing methods and demonstrating promising capabilities in precise flood mapping.
Keywords
sar; flood; flood segmentation; transfer learning; msi
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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