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Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer.

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Title: Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer.
Authors: Li, Quankun1,2,3 (AUTHOR), Bai, Xue1,2 (AUTHOR), Hu, Lizhen1,2,3 (AUTHOR), Li, Liangsheng4 (AUTHOR), Bao, Yaohui5 (AUTHOR), Geng, Xupu1,2,3,6 (AUTHOR) gengxp@xmu.edu.cn, Yan, Xiao-Hai1,3,6 (AUTHOR)
Source: Remote Sensing. Jan2026, Vol. 18 Issue 1, p113. 19p.
Subjects: Ocean dynamics, Image segmentation, Atmospheric physics, Acquisition of data, Data science, Remote sensing by radar
Abstract: Highlights: What are the main findings? A semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena is constructed, using 2383 Sentinel-1 WV mode images and 2628 IW mode sub-images with 100 m resolution and 256 × 256 pixels. Our modified Segformer model named Segformer-OcnP (integrating improved ASPP, CA modules, and progressive upsampling), outperforms classic models like U-Net and original Segformer, achieving 80.98% mDice, 70.32% mIoU, and 86.77% OA. What are the implications of the main findings? The dataset addresses the lack of diverse, multi-phenomenon SAR segmentation data, supporting AI-driven ocean–atmosphere observation research. Segformer-OcnP has improved segmentation accuracy for small-scale and complex phenomena, providing a tool for pixel-level recognition of oceanic and atmospheric processes. Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 Interferometric Wide swath (IW) mode sub-images to construct a semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena, with a balanced distribution of approximately 400 sub-images per category, culminating in a comprehensive dataset of 5011 samples. The images in this dataset have a resolution of 100 m and dimensions of 256 × 256 pixels. We propose Segformer-OcnP model based on Segformer for the semantic segmentation of these multiple oceanic and atmospheric phenomena. Experimental results demonstrate that Segformer-OcnP outperforms classic CNN-based models (U-Net, DeepLabV3+) and mainstream Transformer-based models (SETR, the original Segformer), achieving 80.98% mDice, 70.32% mIoU, and 86.77% Overall Accuracy, verifying its superior segmentation performance. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Ocean+dynamics%22">Ocean dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+physics%22">Atmospheric physics</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Data+science%22">Data science</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing+by+radar%22">Remote sensing by radar</searchLink>
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  Data: Highlights: What are the main findings? A semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena is constructed, using 2383 Sentinel-1 WV mode images and 2628 IW mode sub-images with 100 m resolution and 256 × 256 pixels. Our modified Segformer model named Segformer-OcnP (integrating improved ASPP, CA modules, and progressive upsampling), outperforms classic models like U-Net and original Segformer, achieving 80.98% mDice, 70.32% mIoU, and 86.77% OA. What are the implications of the main findings? The dataset addresses the lack of diverse, multi-phenomenon SAR segmentation data, supporting AI-driven ocean–atmosphere observation research. Segformer-OcnP has improved segmentation accuracy for small-scale and complex phenomena, providing a tool for pixel-level recognition of oceanic and atmospheric processes. Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 Interferometric Wide swath (IW) mode sub-images to construct a semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena, with a balanced distribution of approximately 400 sub-images per category, culminating in a comprehensive dataset of 5011 samples. The images in this dataset have a resolution of 100 m and dimensions of 256 × 256 pixels. We propose Segformer-OcnP model based on Segformer for the semantic segmentation of these multiple oceanic and atmospheric phenomena. Experimental results demonstrate that Segformer-OcnP outperforms classic CNN-based models (U-Net, DeepLabV3+) and mainstream Transformer-based models (SETR, the original Segformer), achieving 80.98% mDice, 70.32% mIoU, and 86.77% Overall Accuracy, verifying its superior segmentation performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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    Subjects:
      – SubjectFull: Ocean dynamics
        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Atmospheric physics
        Type: general
      – SubjectFull: Acquisition of data
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      – SubjectFull: Data science
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      – SubjectFull: Remote sensing by radar
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      – TitleFull: Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer.
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            NameFull: Li, Quankun
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              M: 01
              Text: Jan2026
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              Y: 2026
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