当前位置: X-MOL 学术J. Appl. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
SA-U-Net++: SAR marine floating raft aquaculture identification based on semantic segmentation and ISAR augmentation
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2021-01-01 , DOI: 10.1117/1.jrs.15.016505
Deyi Wang 1 , Min Han 2
Affiliation  

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:

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。
更新日期:2021-01-18
down
wechat
bug