AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES
Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenge...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/201/2022/isprs-annals-V-3-2022-201-2022.pdf |
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| author | B. Ghosh B. Ghosh S. Garg M. Motagh M. Motagh |
| author_facet | B. Ghosh B. Ghosh S. Garg M. Motagh M. Motagh |
| author_sort | B. Ghosh |
| collection | DOAJ |
| description | Floods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water. The generalization ability of binary segmentation using threshold split-based method, is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection. However, there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team, and IEEE GRSS Earth Science Informatics Technical Committee. The dataset covers flood events from Nebraska, North Alabama, Bangladesh, Red River North, and Florence. The performances of both networks were evaluated with multiple training, testing, and validation. During testing, the UNet model achieved the meanIOU score of 75.06% and the FPN model achieved the meanIOU score of 75.76%. |
| first_indexed | 2024-04-14T00:19:54Z |
| format | Article |
| id | doaj.art-4777231b2e134c4fbe6f1c1fae414b0e |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-04-14T00:19:54Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-4777231b2e134c4fbe6f1c1fae414b0e2022-12-22T02:22:59ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502022-05-01V-3-202220120810.5194/isprs-annals-V-3-2022-201-2022AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURESB. Ghosh0B. Ghosh1S. Garg2M. Motagh3M. Motagh4GFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing, 14473 Potsdam, GermanyDepartment of Computer Science, Technical University Berlin, Berlin, GermanyFuture Infrastructure and Built Environment, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UKGFZ German Research Centre for Geosciences, Department of Geodesy, Section of Remote Sensing, 14473 Potsdam, GermanyInstitut of Photogrammetry and GeoInformation (IPI), Leibniz University Hannover, GermanyFloods are the most frequent, costliest natural disasters having devastating consequences on people, infrastructure, and the ecosystem. During flood events near real-time satellite imagery has proven to be an efficient management tool for disaster management authorities. However one of the challenges is accurate classification and segmentation of flooded water. The generalization ability of binary segmentation using threshold split-based method, is limited due to the effects of backscatter, geographical area, and time of image collection. Recent advancements in deep learning algorithms for image segmentation has demonstrated excellent potential for improving flood detection. However, there have been limited studies in this domain due to the lack of large scale labeled flood event dataset. In this paper, we present two deep learning approaches, first using a UNet and second, using a Feature Pyramid Network (FPN), both based on a backbone of EfficientNet-B7, by leveraging publicly available Sentinel-1 dataset provided jointly by NASA Interagency Implementation and Advanced Concepts Team, and IEEE GRSS Earth Science Informatics Technical Committee. The dataset covers flood events from Nebraska, North Alabama, Bangladesh, Red River North, and Florence. The performances of both networks were evaluated with multiple training, testing, and validation. During testing, the UNet model achieved the meanIOU score of 75.06% and the FPN model achieved the meanIOU score of 75.76%.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/201/2022/isprs-annals-V-3-2022-201-2022.pdf |
| spellingShingle | B. Ghosh B. Ghosh S. Garg M. Motagh M. Motagh AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES |
| title_full | AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES |
| title_fullStr | AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES |
| title_full_unstemmed | AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES |
| title_short | AUTOMATIC FLOOD DETECTION FROM SENTINEL-1 DATA USING DEEP LEARNING ARCHITECTURES |
| title_sort | automatic flood detection from sentinel 1 data using deep learning architectures |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2022/201/2022/isprs-annals-V-3-2022-201-2022.pdf |
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