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

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Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer.pdf (9.13 MB)
Date
2025-12-28
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Publisher
Remote Sensing
Abstract
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.
Description
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. https://creativecommons.org/licenses/by/4.0/ This article was originally published in Remote Sensing. The version of record is available at: https://doi.org/10.3390/rs18010113
Keywords
SAR imagery, oceanic and atmospheric phenomenon identification and detection, semantic segmentation
Citation
Li, Q., Bai, X., Hu, L., Li, L., Bao, Y., Geng, X., & Yan, X.-H. (2026). Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer. Remote Sensing, 18(1), 113. https://doi.org/10.3390/rs18010113
URI
https://udspace.udel.edu/handle/19716/36834
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Open Access Publications - College of Earth, Ocean, and Environment