Research on Sentinel-1/SAR sea ice detection method in Liaodong Bay based on AUNet++
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摘要: 冬季海冰会极大影响辽东湾地区近岸工程建筑、石油平台、船舶航行等安全生产活动。星载合成孔径雷达不受天气影响且分辨率高，可用于辽东湾海冰灾害监测。本文在深度学习模型UNet++的基础上引入卷积注意力模块（CBAM），并使用交叉损失函数来优化模型，建立辽东湾Sentinel-1 SAR图像高精度海冰检测模型（AUNet++），并与PSPNet、Deeplabv3+、DAU-Net等多种深度学习方法进行对比。实验结果表明AUNet++海冰检测方法在OA、AA、MIoU、Kappa系数4种指标上分别达到了97.56%、97.53%、95.19%、95.07%，结果优于其他深度学习方法。该方法可以在高风速的干扰下对海冰边缘、光滑冰面完成精确海冰信息提取，能够为辽东湾地区的大范围、高精度海冰检测工作提供技术支撑。
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关键词:
- 海冰检测 /
- Sentinel-1 /
- 合成孔径雷达 /
- 深度学习
Abstract: The sea ice in Bohai Sea in winter affects the safety production activities of oil platform and ship navigation, as well as the safety of offshore engineering and construction. Spaceborne SAR is not affected by weather and has high resolution, which can be used for sea ice disaster monitoring in Bohai Sea. Based on deep learning model UNet++, this paper introduces Convolutional attention module (CBAM) and uses cross loss function to optimize the model, and establishes a high-precision sea ice detection model for Sentinel-1 SAR data in the Liaodong Bay (AUNet++). And compared with PSPNet, Deeplabv3+, DAU-Net and other deep learning methods. The experimental results show that AUNet++ sea ice detection method achieves 97.56%, 97.53%, 95.19% and 95.07% in OA, AA, MIoU and Kappa coefficients, respectively, which is superior to other deep learning methods. This method can extract accurate sea ice information from sea ice edge and smooth ice under the interference of high wind speed, and can provide technical support for large-scale and high-precision sea ice detection in Liaodong Bay area.-
Key words:
- Sea ice detection /
- Sentinel-1 /
- Synthetic aperture radar /
- Deep learning
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图 1 研究区域地理位置（渤海辽东湾）
Fig. 1 The geographical location of the study area (Liaodong Bay, Bohai Sea)
图 2 数据预处理前后对比
a. VV极化，b. RGB假彩色
Fig. 2 Comparison before and after data preprocessing
a. VV band, b. RGB false color
图 6 各类样本不同方法结果可视化
a. 冰水间隙，b. 光滑冰面，c. 碎冰，d. 海冰边缘，e. 高风速区域
Fig. 6 Visualization of results of different methods for various samples
a. ice water gap, b. smooth ice surface, c. broken ice, d. sea ice margin, e. high wind speed area
图 9 切片拼接过程（未进行像素重叠处理）
Fig. 9 Slice splicing process (without pixel overlap processing)
图 10 不同重叠区域处理方法结果对比
Fig. 10 Comparison of the results of different overlapping region processing methods
图 11 2020年辽东湾冰期3景Sentinel-1数据整体检测结果
Fig. 11 Overall detection results of Sentinel-1 data of three views during the Liaodong Bay ice Age in 2020
图 12 辽东湾3景Sentinel-1数据整体检测结果
Fig. 12 Overall detection results of Sentinel-1 data of three views in Liaodong Bay
表 1 研究数据具体参数信息
Tab. 1 Specific parameter information of research data
序号 成像时间 数据模式 极化方式 分辨率 卫星 1 2019年12月9日09：49 IW VV+VH 5 × 20 m A星 2 2020年1月2日09：49 IW VV+VH 5 × 20 m A星 3 2020年1月8日09：48 IW VV+VH 5 × 20 m B星 4 2020年1月14日09：48 IW VV+VH 5 × 20 m A星 5 2020年1月20日09：48 IW VV+VH 5 × 20 m B星 6 2020年1月26日09：49 IW VV+VH 5 × 20 m A星 7 2020年2月1日09：48 IW VV+VH 5 × 20 m B星 8 2020年2月7日09：49 IW VV+VH 5 × 20 m A星 9 2020年2月13日09：48 IW VV+VH 5 × 20 m B星 10 2020年2月19日09：49 IW VV+VH 5 × 20 m A星 11 2023年2月3日09：49 IW VV+VH 5 × 20 m A星 12 2024年1月5日09：49 IW VV+VH 5 × 20 m A星 13 2024年1月17日09：49 IW VV+VH 5 × 20 m A星 
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表 2 不同方法对比结果
Tab. 2 Comparison results of different methods
方法 总体精度/% 平均精度/% 均交并比/% Kappa系数/% SegNet 96.65 96.68 93.47 93.24 PSPNet 93.57 93.59 87.84 84.05 DeepLabv3+ 94.48 94.50 92.94 92.67 UNet 96.48 96.49 93.15 92.90 DAU-UNet 96.37 96.35 92.94 92.67 AUNet++ 97.56 97.53 95.19 95.07
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表 3 消融实验结果分析
Tab. 3 Analysis of ablation results
方法 总体精度/% 平均精度/% 均交并比/% Kappa系数 UNet++ 97.17 97.17 94.46 94.30 UNet++_BCED 97.23 97.24 94.57 94.41 AUNet++ 97.37 97.35 94.83 94.69 AUNet++_BCED 97.56 97.53 95.19 95.07
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