CCESAR: Coastline Classification-Extraction From SAR Images Using CNN-U-Net Combination
This is an incremental improvement for remote sensing applications, addressing coastline detection in SAR imagery.
The paper tackles coastline extraction from SAR images by proposing a two-stage model combining CNN and U-Net for classification and segmentation, showing it outperforms a single U-Net model.
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.