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

arXiv: https://arxiv.org/abs/2501.12384 · HTML: https://mlsanity.com/paper/2501.12384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 8 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 1 of 1)

- [HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline](https://mlsanity.com/paper/2103.01849/) (2021)

## Abstract (arXiv)

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.