Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning
Flood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study,...
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2023-02-01
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| author | Mohammadali Abbasi Reza Shah-Hosseini Mohammad Aghdami-Nia |
| author_facet | Mohammadali Abbasi Reza Shah-Hosseini Mohammad Aghdami-Nia |
| author_sort | Mohammadali Abbasi |
| collection | DOAJ |
| description | Flood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study, the ETCI 2021 flood event detection competition dataset, organized by the NASA Advanced Concepts and Implementation Team in collaboration with the IEEE GRSS Geoscience Informatics Technical Committee, has been used. Moreover, we have utilized the U-Net and X-Net architecture as a segmentation model to map flooded regions. This study aimed to identify the optimum polarization of the Sentinel-1 satellite for flood detection. By examining and comparing the obtained results, it was observed that the VV polarization offered better results in both models. Furthermore, U-Net had a better performance than X-Net in both polarizations. |
| format | Article |
| id | doaj-art-4b4be775608d44aa8049568e66d54b40 |
| institution | DOAJ |
| issn | 2504-3900 |
| language | English |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
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| series | Proceedings |
| spelling | doaj-art-4b4be775608d44aa8049568e66d54b402025-08-20T02:42:25ZengMDPI AGProceedings2504-39002023-02-018711410.3390/IECG2022-14069Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep LearningMohammadali Abbasi0Reza Shah-Hosseini1Mohammad Aghdami-Nia2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, IranFlood is one of the most damaging natural hazards, and timely detection of it is very important to save human lives and assess the level of damage. The occurrence of floods in cloudy weather conditions makes the use of radar-based sensors for real-time flood mapping inevitable. In the present study, the ETCI 2021 flood event detection competition dataset, organized by the NASA Advanced Concepts and Implementation Team in collaboration with the IEEE GRSS Geoscience Informatics Technical Committee, has been used. Moreover, we have utilized the U-Net and X-Net architecture as a segmentation model to map flooded regions. This study aimed to identify the optimum polarization of the Sentinel-1 satellite for flood detection. By examining and comparing the obtained results, it was observed that the VV polarization offered better results in both models. Furthermore, U-Net had a better performance than X-Net in both polarizations.https://www.mdpi.com/2504-3900/87/1/14flood detectionremote sensingSARSentinel-1deep learningU-Net |
| spellingShingle | Mohammadali Abbasi Reza Shah-Hosseini Mohammad Aghdami-Nia Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning Proceedings flood detection remote sensing SAR Sentinel-1 deep learning U-Net |
| title | Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning |
| title_full | Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning |
| title_fullStr | Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning |
| title_full_unstemmed | Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning |
| title_short | Sentinel-1 Polarization Comparison for Flood Segmentation Using Deep Learning |
| title_sort | sentinel 1 polarization comparison for flood segmentation using deep learning |
| topic | flood detection remote sensing SAR Sentinel-1 deep learning U-Net |
| url | https://www.mdpi.com/2504-3900/87/1/14 |
| work_keys_str_mv | AT mohammadaliabbasi sentinel1polarizationcomparisonforfloodsegmentationusingdeeplearning AT rezashahhosseini sentinel1polarizationcomparisonforfloodsegmentationusingdeeplearning AT mohammadaghdaminia sentinel1polarizationcomparisonforfloodsegmentationusingdeeplearning |