PDE-Based Physics Guided Neural Network for SAR Image Segmentation
Academicians and researchers have been keen on climatic and maritime monitoring. Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean. Sea-ice classification using Sentinel-1, a type of SAR, has gained popularity over the years. Automatic image segmentation is a fu...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10844292/ |
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| author | Rachana Rao B. Roja Reddy M. Uttara Kumari |
| author_facet | Rachana Rao B. Roja Reddy M. Uttara Kumari |
| author_sort | Rachana Rao |
| collection | DOAJ |
| description | Academicians and researchers have been keen on climatic and maritime monitoring. Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean. Sea-ice classification using Sentinel-1, a type of SAR, has gained popularity over the years. Automatic image segmentation is a fundamental task in SAR image analysis. This paper proposes a novel, innovative approach to SAR image segmentation by integrating physics-based knowledge into a neural network framework. The implementation of a PDE for SAR image segmentation offers a promising avenue for advancing the field of remote sensing. PDEs provide the required physics for the model. This integration not only enhances the network’s capability to learn meaningful features but also enables it to generate more interpretable and physically meaningful segmentation maps. The Partial Differential Equations (PDE) based Physics-Guided Neural Network (PGNN) model can achieve an accuracy of about 96%, which is greater than all existing state-of-art techniques. By harnessing the synergy between deep learning and physics-based knowledge, this work not only improves segmentation accuracy but also facilitates a deeper understanding of SAR data, paving the way for more reliable and insightful remote sensing applications. |
| format | Article |
| id | doaj-art-f7e196138ad140f4876351425f1a49b1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f7e196138ad140f4876351425f1a49b12025-01-25T00:02:27ZengIEEEIEEE Access2169-35362025-01-0113126821269110.1109/ACCESS.2025.353120810844292PDE-Based Physics Guided Neural Network for SAR Image SegmentationRachana Rao0https://orcid.org/0000-0003-3321-354XB. Roja Reddy1M. Uttara Kumari2R. V. College of Engineering, Bengaluru, IndiaR. V. College of Engineering, Bengaluru, IndiaR. V. College of Engineering, Bengaluru, IndiaAcademicians and researchers have been keen on climatic and maritime monitoring. Synthetic Aperture Radars (SAR) have been instrumental in capturing images of the ocean. Sea-ice classification using Sentinel-1, a type of SAR, has gained popularity over the years. Automatic image segmentation is a fundamental task in SAR image analysis. This paper proposes a novel, innovative approach to SAR image segmentation by integrating physics-based knowledge into a neural network framework. The implementation of a PDE for SAR image segmentation offers a promising avenue for advancing the field of remote sensing. PDEs provide the required physics for the model. This integration not only enhances the network’s capability to learn meaningful features but also enables it to generate more interpretable and physically meaningful segmentation maps. The Partial Differential Equations (PDE) based Physics-Guided Neural Network (PGNN) model can achieve an accuracy of about 96%, which is greater than all existing state-of-art techniques. By harnessing the synergy between deep learning and physics-based knowledge, this work not only improves segmentation accuracy but also facilitates a deeper understanding of SAR data, paving the way for more reliable and insightful remote sensing applications.https://ieeexplore.ieee.org/document/10844292/Partial differential equationsphysics guided neural networkphysics informed learningradio detection and rangingsynthetic aperture radar |
| spellingShingle | Rachana Rao B. Roja Reddy M. Uttara Kumari PDE-Based Physics Guided Neural Network for SAR Image Segmentation IEEE Access Partial differential equations physics guided neural network physics informed learning radio detection and ranging synthetic aperture radar |
| title | PDE-Based Physics Guided Neural Network for SAR Image Segmentation |
| title_full | PDE-Based Physics Guided Neural Network for SAR Image Segmentation |
| title_fullStr | PDE-Based Physics Guided Neural Network for SAR Image Segmentation |
| title_full_unstemmed | PDE-Based Physics Guided Neural Network for SAR Image Segmentation |
| title_short | PDE-Based Physics Guided Neural Network for SAR Image Segmentation |
| title_sort | pde based physics guided neural network for sar image segmentation |
| topic | Partial differential equations physics guided neural network physics informed learning radio detection and ranging synthetic aperture radar |
| url | https://ieeexplore.ieee.org/document/10844292/ |
| work_keys_str_mv | AT rachanarao pdebasedphysicsguidedneuralnetworkforsarimagesegmentation AT brojareddy pdebasedphysicsguidedneuralnetworkforsarimagesegmentation AT muttarakumari pdebasedphysicsguidedneuralnetworkforsarimagesegmentation |