Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images
As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas...
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2025-01-01
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| author | G. Savitha S. Girisha Pundarika Sughosh Dasharathraj K. Shetty Jayaraj Mymbilly Balakrishnan Rahul Paul Nithesh Naik |
| author_facet | G. Savitha S. Girisha Pundarika Sughosh Dasharathraj K. Shetty Jayaraj Mymbilly Balakrishnan Rahul Paul Nithesh Naik |
| author_sort | G. Savitha |
| collection | DOAJ |
| description | As one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster response and mitigation efforts. Accurately defining the extent of floods is a problem for traditional flood mapping approaches, which emphasizes the vital need for modern technologies such as Synthetic Aperture Radar (SAR) imaging. Additionally, there is a need to develop computer-aided tools specifically designed for automatically identifying areas that are vulnerable to flooding using SAR data. Nonetheless, the lack of consistent large datasets presents a barrier that prevents these algorithms from progressing and being used in real-world scenarios. For this reason, the present study aims to develop a semi-supervised semantic segmentation algorithm for accurate flood region delineation in SAR data. In particular, the paper proposes labeling unannotated instances of data using a pseudo-label generation strategy. In order to accomplish this, the study suggests using a self-supervised trained teacher model to generate pseudo-labels and speed up the training procedure. The teacher model is then trained with a student model to efficiently extract features from the labeled data. Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. A comprehensive assessment conducted on publicly available datasets produces promising results. These results confirm the usefulness and possible relevance of the suggested methodology in enhancing efforts related to flood zone identification and management. |
| format | Article |
| id | doaj-art-a4ed3a35865d411ab1bed82da82d696c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a4ed3a35865d411ab1bed82da82d696c2025-01-21T00:01:54ZengIEEEIEEE Access2169-35362025-01-01139642965310.1109/ACCESS.2025.352624410829568Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR ImagesG. Savitha0S. Girisha1https://orcid.org/0000-0003-2582-9600Pundarika Sughosh2https://orcid.org/0000-0003-2024-2974Dasharathraj K. Shetty3https://orcid.org/0000-0002-5021-4029Jayaraj Mymbilly Balakrishnan4Rahul Paul5Nithesh Naik6https://orcid.org/0000-0003-0356-7697Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Emergency Medicine, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USADepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaAs one of the most powerful natural catastrophes, floods pose serious risks to people’s lives, the integrity of infrastructure, and agricultural landscapes, which increases the toll they take on the economy and society. As a result, it becomes essential to continuously monitor these areas of vulnerability in order to support effective disaster response and mitigation efforts. Accurately defining the extent of floods is a problem for traditional flood mapping approaches, which emphasizes the vital need for modern technologies such as Synthetic Aperture Radar (SAR) imaging. Additionally, there is a need to develop computer-aided tools specifically designed for automatically identifying areas that are vulnerable to flooding using SAR data. Nonetheless, the lack of consistent large datasets presents a barrier that prevents these algorithms from progressing and being used in real-world scenarios. For this reason, the present study aims to develop a semi-supervised semantic segmentation algorithm for accurate flood region delineation in SAR data. In particular, the paper proposes labeling unannotated instances of data using a pseudo-label generation strategy. In order to accomplish this, the study suggests using a self-supervised trained teacher model to generate pseudo-labels and speed up the training procedure. The teacher model is then trained with a student model to efficiently extract features from the labeled data. Furthermore, the study presents a new semantic segmentation technique that uses convolutional neural networks to automatically identify flooded areas in SAR images. A comprehensive assessment conducted on publicly available datasets produces promising results. These results confirm the usefulness and possible relevance of the suggested methodology in enhancing efforts related to flood zone identification and management.https://ieeexplore.ieee.org/document/10829568/Semi-supervised learningflood mappingSAR imagessemantic segmentation |
| spellingShingle | G. Savitha S. Girisha Pundarika Sughosh Dasharathraj K. Shetty Jayaraj Mymbilly Balakrishnan Rahul Paul Nithesh Naik Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images IEEE Access Semi-supervised learning flood mapping SAR images semantic segmentation |
| title | Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images |
| title_full | Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images |
| title_fullStr | Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images |
| title_full_unstemmed | Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images |
| title_short | Consistency Regularization for Semi-Supervised Semantic Segmentation of Flood Regions From SAR Images |
| title_sort | consistency regularization for semi supervised semantic segmentation of flood regions from sar images |
| topic | Semi-supervised learning flood mapping SAR images semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10829568/ |
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