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arXiv · 2501.12384

CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination

Vidhu Arora·Shreyan Gupta·Ananthakrishna Kudupu·Aditya Priyadarshi·Aswathi Mundayatt·Jaya Sreevalsan-Nair
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Abstract

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.

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Vidhu Arora, Shreyan Gupta, Ananthakrishna Kudupu, Aditya Priyadarshi, Aswathi Mundayatt, Jaya Sreevalsan-Nair. 2025-01-21. CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination. https://arxiv.org/abs/2501.12384

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