Preprints
https://doi.org/10.5194/essd-2024-222
https://doi.org/10.5194/essd-2024-222
01 Jul 2024
 | 01 Jul 2024
Status: this preprint is currently under review for the journal ESSD.

SAR Image Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena

Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng, and Xiao-Hai Yan

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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Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng, and Xiao-Hai Yan

Status: open (until 25 Aug 2024)

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Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng, and Xiao-Hai Yan

Data sets

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

Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng, and Xiao-Hai Yan

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Short summary
Overall, the SAR image dataset proposed in this study makes a significant contribution to oceanography, providing valuable data resources for studying the dynamic processes of multi-scale oceanic and atmospheric phenomena, validating deep learning models, and developing high-resolution models. This dataset is anticipated to stimulate further research and advancements in understanding the complex dynamics of sea surface.
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