Marine floating raft aquaculture (FRA) monitoring is vital for environment protection and mariculture management. Synthetic aperture radar (SAR) could provide high-quality remote sensing images under all weather conditions compared with the existing optical remote-sensing-based methods. Traditional SAR monitoring methods extract the pixel feature of marine FRA in single patches, which commonly leads to poor generalization. We propose a self-attention semantic segmentation method based on modified U-Net++ (SA-U-Net++) for FRA segmentation, which could automatically extract semantic feature information, and provide superior performance under complicated scenes. The proposed self-attention backbone could help to extract more precise features and enhance the overall accuracy. Furthermore, we propose a FRA-ISAR data generation method based on inverse SAR (ISAR) imaging to alleviate the sample shortage problem. We introduce the semantic segmentation method into FRA-SAR segmentation for the first time. The experiments verify the effectiveness and superiority of SAR FRA segmentation based on the proposed SA-U-Net++ model compared with the existed semantic segmentation approaches.

中文翻译：

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SA-U-Net ++：基于语义分割和ISAR增强的SAR海洋浮筏水产养殖识别

海上浮筏水产养殖（FRA）监测对于环境保护和海水养殖管理至关重要。与现有的基于光学遥感的方法相比，合成孔径雷达（SAR）可以在所有天气条件下提供高质量的遥感图像。传统的SAR监测方法会在单个斑块中提取海洋FRA的像素特征，这通常会导致泛化性差。我们提出了一种基于改进的U-Net ++（SA-U-Net ++）的自注意语义分割方法进行FRA分割，该方法可以自动提取语义特征信息，并在复杂场景下提供出色的性能。提出的自我注意主干可以帮助提取更精确的特征并提高整体准确性。此外，我们提出了一种基于反SAR（ISAR）成像的FRA-ISAR数据生成方法，以缓解样品短缺的问题。我们首次将语义分割方法引入到FRA-SAR分割中。实验证明，与现有的语义分割方法相比，基于提出的SA-U-Net ++模型，SAR FRA分割的有效性和优越性。