the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
SAR Image Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena
Abstract. The ocean surface exhibits a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is crucial for understanding oceanic dynamics and ocean-atmosphere interactions. In this study, we select 2,383 Sentinel-1 WV mode images and 2,628 IW mode sub-images to construct a semantic segmentation dataset that includes 12 typical oceanic and atmospheric phenomena. Each phenomenon is represented by approximately 400 sub-images, resulting in a total of 5,011 images. The images in this dataset have a resolution of 100 meters and dimensions of 256 × 256 pixels. We propose a modified Segformer model to segment semantically these multiple categories of oceanic and atmospheric phenomena. Experimental results show that the modified Segformer model achieves an average Dice coefficient of 80.98 %, an average IoU of 70.32 %, and an overall accuracy of 87.13 %, demonstrating robust segmentation performance of typical oceanic and atmospheric phenomena in SAR images.
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Status: open (until 07 Aug 2024)
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A dataset for semantic segmentation of typical oceanic and atmospheric phenomena from Sentinel-1 images Quankun Li, Xue Bai, and Xupu Geng https://doi.org/10.5281/zenodo.11410662
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