Conference paper Open Access
A tempοral Deep Convolutional Neural Network model on Sentinel-1 Image Time Series for pixel-wise Flood Classification
Vlachos, Konstantinos;
Moumtzidou, Anastasia;
Gialampoukidis, Ilias;
Vrochidis, Stefanos;
Kompatsiaris, Ioannis
Accurate and timely flood mapping is important in emergency management during and after extreme flood events which can be greatly served by Synthetic Aperture Radar (SAR). This work is focused on open-land regions where
a custom annotation based on expert knowledge and NDFI is used. Research on SAR flood detection is mostly based
on histogram thresholding that has low time complexity and seems ideal for emergency response, although human intervention is needed. Machine learning methods have fewer errors and minimize the need for human intervention but their computational complexity is higher. This work aims to provide a lightweight convolutional neural network baseline for pixel-wise time series classification in flood monitoring on SAR satellite image time series. Quantitative and qualitative evaluation of results indicate that the approach is promising.
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- Publication date:
- July 17, 2022
- DOI:
-
- Keyword(s):
- Sentinel-1 SAR flood detection time series classification deep learning CNN
- Grants:
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European Commission:
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aqua3S - Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks (832876)
- WQeMS - Copernicus Assisted Lake Water Quality Emergency Monitoring Service (101004157)
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aqua3S - Enhancing Standardisation strategies to integrate innovative technologies for Safety and Security in existing water networks (832876)
- Meeting:
- IEEE International Geoscience and Remote Sensing Symposium 2022 (IGARSS2022), Kuala Lumpur, Malaysia, 17-22 July 2022
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International