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

Source: [CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination](<https://www.sota2.com/research/paper/68fa76a61e209131cea6e2c7>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:58:19.729Z

Authors: Vidhu Arora, Shreyan Gupta, Ananthakrishna Kudupu, Aditya Priyadarshi, Aswathi Mundayatt, Jaya Sreevalsan-Nair

Published: 2025-01-21T18:57:34.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.12384>)

[PDF](<https://arxiv.org/pdf/2501.12384.pdf>)