用分类+分割两阶段模型提升雷达图像海岸线提取精度
CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination
- 先分类后分割，区分不同海岸线类型
- 在哨兵1号图像上优于单一U-Net模型
- 适合需要高精度海岸线数据的研究者
本文提出一种两阶段海岸线提取方法（CCESAR），通过结合卷积神经网络（CNN）与U-Net模型，先对合成孔径雷达（SAR）图像进行海岸线类型分类，再进行精细分割。我们假设单一分割模型难以有效刻画不同类型的海岸线特征，实验表明该两阶段流程在多种图像压缩水平下均表现更优。基于哨兵1号（Sentinel-1）图像的测试结果证明，该方法在海岸线提取任务中显著优于单个U-Net模型。
原文摘要 · Abstract (English)
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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