CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912197728272384 |
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| author | Arora, Vidhu Gupta, Shreyan Kudupu, Ananthakrishna Priyadarshi, Aditya Mundayatt, Aswathi Sreevalsan-Nair, Jaya |
| author_facet | Arora, Vidhu Gupta, Shreyan Kudupu, Ananthakrishna Priyadarshi, Aditya Mundayatt, Aswathi Sreevalsan-Nair, Jaya |
| contents | In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to characterize different coastline types. We demonstrate that the need for a two-stage workflow prevails through different compression levels of these images. Our results from experiments using a combination of CNN and U-Net models on Sentinel-1 images show that the two-stage workflow, coastline classification-extraction from SAR images (CCESAR) outperforms a single U-Net segmentation model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_12384 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination Arora, Vidhu Gupta, Shreyan Kudupu, Ananthakrishna Priyadarshi, Aditya Mundayatt, Aswathi Sreevalsan-Nair, Jaya Computer Vision and Pattern Recognition Machine Learning Image and Video Processing In this article, we improve the deep learning solution for coastline extraction from Synthetic Aperture Radar (SAR) images by proposing a two-stage model involving image classification followed by segmentation. We hypothesize that a single segmentation model usually used for coastline detection is insufficient to characterize different coastline types. We demonstrate that the need for a two-stage workflow prevails through different compression levels of these images. Our results from experiments using a combination of CNN and U-Net models on Sentinel-1 images show that the two-stage workflow, coastline classification-extraction from SAR images (CCESAR) outperforms a single U-Net segmentation model. |
| title | CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2501.12384 |