Flood extent mapping in SAR images using semi-supervised approach
Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, an...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302501374X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850149413141348352 |
|---|---|
| author | Girisha S Savitha G Sughosh P |
| author_facet | Girisha S Savitha G Sughosh P |
| author_sort | Girisha S |
| collection | DOAJ |
| description | Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, and estimating the degree of damage. Accurate semantic segmentation of flood-zones from SAR images is necessary for disaster management as it reduces human error and facilitates fast decision-making. However, the complexity of SAR images and the scarcity of annotated datasets make it difficult to develop efficient computer vision algorithms for this use. The paper employs consistency regularization and pseudo-label generation to overcome these issues and presents a novel hybrid semi-supervised semantic segmentation method. In particular, the study presents a student-teacher paradigm in which robust feature learning is ensured by training the teacher model through consistency regularization and self-supervised learning. An attention module-based tailored student model is developed to precisely identify flood-zones. Auxiliary decoders are also added throughout the training phase to reinforce consistency regularization and help the model capture extensive hierarchical characteristics. The suggested method was assessed with publicly accessible datasets, and the outcomes show notable gains in both qualitative and quantitative measures. The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management. |
| format | Article |
| id | doaj-art-bbc97f7b5cfd451a9e0723d546efff69 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-bbc97f7b5cfd451a9e0723d546efff692025-08-20T02:26:56ZengElsevierResults in Engineering2590-12302025-06-012610530410.1016/j.rineng.2025.105304Flood extent mapping in SAR images using semi-supervised approachGirisha S0Savitha G1Sughosh P2Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, IndiaDepartment of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Corresponding author.Floods pose a significant threat to both human populations and critical infrastructure. They are caused by excessive precipitation, snowmelt, or infrastructure failures. Precise mapping of flood levels is essential for directing emergency response, allocating resources as efficiently as possible, and estimating the degree of damage. Accurate semantic segmentation of flood-zones from SAR images is necessary for disaster management as it reduces human error and facilitates fast decision-making. However, the complexity of SAR images and the scarcity of annotated datasets make it difficult to develop efficient computer vision algorithms for this use. The paper employs consistency regularization and pseudo-label generation to overcome these issues and presents a novel hybrid semi-supervised semantic segmentation method. In particular, the study presents a student-teacher paradigm in which robust feature learning is ensured by training the teacher model through consistency regularization and self-supervised learning. An attention module-based tailored student model is developed to precisely identify flood-zones. Auxiliary decoders are also added throughout the training phase to reinforce consistency regularization and help the model capture extensive hierarchical characteristics. The suggested method was assessed with publicly accessible datasets, and the outcomes show notable gains in both qualitative and quantitative measures. The findings highlight the method's effectiveness in improving flood-zone segmentation accuracy, leading to more efficient disaster management.http://www.sciencedirect.com/science/article/pii/S259012302501374XSARSemantic segmentationDeep learningSemi-supervised learningConsistency regularizationPseudo-label generation |
| spellingShingle | Girisha S Savitha G Sughosh P Flood extent mapping in SAR images using semi-supervised approach Results in Engineering SAR Semantic segmentation Deep learning Semi-supervised learning Consistency regularization Pseudo-label generation |
| title | Flood extent mapping in SAR images using semi-supervised approach |
| title_full | Flood extent mapping in SAR images using semi-supervised approach |
| title_fullStr | Flood extent mapping in SAR images using semi-supervised approach |
| title_full_unstemmed | Flood extent mapping in SAR images using semi-supervised approach |
| title_short | Flood extent mapping in SAR images using semi-supervised approach |
| title_sort | flood extent mapping in sar images using semi supervised approach |
| topic | SAR Semantic segmentation Deep learning Semi-supervised learning Consistency regularization Pseudo-label generation |
| url | http://www.sciencedirect.com/science/article/pii/S259012302501374X |
| work_keys_str_mv | AT girishas floodextentmappinginsarimagesusingsemisupervisedapproach AT savithag floodextentmappinginsarimagesusingsemisupervisedapproach AT sughoshp floodextentmappinginsarimagesusingsemisupervisedapproach |