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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18010113 |
