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基于深度学习的GF-1卫星WFV影像赤潮探测方法

崔宾阁 杨光 方喜 刘荣杰

崔宾阁,杨光,方喜,等. 基于深度学习的GF-1卫星WFV影像赤潮探测方法[J]. 海洋学报,2023,45(7):147–157 doi: 10.12284/hyxb2023070
引用本文: 崔宾阁,杨光,方喜,等. 基于深度学习的GF-1卫星WFV影像赤潮探测方法[J]. 海洋学报,2023,45(7):147–157 doi: 10.12284/hyxb2023070
Cui Bin’ge,Yang Guang,Fang Xi, et al. Red tide detection using GF-1 WFV image based on deep learning method[J]. Haiyang Xuebao,2023, 45(7):147–157 doi: 10.12284/hyxb2023070
Citation: Cui Bin’ge,Yang Guang,Fang Xi, et al. Red tide detection using GF-1 WFV image based on deep learning method[J]. Haiyang Xuebao,2023, 45(7):147–157 doi: 10.12284/hyxb2023070

基于深度学习的GF-1卫星WFV影像赤潮探测方法

doi: 10.12284/hyxb2023070
基金项目: 国家自然科学基金重大项目(61890964);中韩海洋科学共同研究中心项目(PI-2022-1)。
详细信息
    作者简介:

    崔宾阁(1979-),男,教授,现从事高光谱遥感、海洋和海岸带遥感监测技术研究。E-mail:cuibinge@sdust.edu.cn

    通讯作者:

    刘荣杰(1981-),男,副研究员,主要从事自主卫星数据处理、水体光学遥感研究。E-mail: liurj@fio.org.cn

  • 中图分类号: TP751; P714+.5

Red tide detection using GF-1 WFV image based on deep learning method

  • 摘要: 赤潮是我国主要的海洋生态灾害,有效监测赤潮的发生和空间分布对于赤潮的防治具有重要意义。传统的赤潮监测以低空间分辨率的水色卫星为主,但是其对于频发的小规模赤潮存在监控盲区。GF-1卫星WFV影像具有空间分辨率高、成像幅宽大和重访周期短等优点,在小规模赤潮监测中表现出较大的潜力。然而,GF-1卫星WFV影像的光谱分辨率较低,波段少,传统面向水色卫星的赤潮探测方法无法应用于GF-1卫星WFV数据。而且赤潮具有形态多变、尺度不一的特点,难以精确提取。基于此,本文提出了一种面向GF-1卫星WFV影像的尺度自适应赤潮探测网络(SARTNet)。该网络采用双层主干结构以融合赤潮水体的形状特征与细节特征,并引入注意力机制挖掘不同尺度赤潮特征之间的相关性,提高网络对复杂分布赤潮的探测能力。实验结果表明,SARTNet赤潮探测精度优于现有方法,F1分数达到0.89以上,对不同尺度的赤潮漏提和误提较少,且受环境因素的影响较小。
  • 图  1  研究区域

    Fig.  1  Study area

    图  2  研究区I赤潮GF-1 WFV影像

    Fig.  2  GF-1 WFV images of the location of study area I and red tides

    图  3  研究区II赤潮GF-1 WFV影像

    Fig.  3  GF-1 WFV images of the location of study area II and red tides

    图  4  测试图像及其真值图

    Fig.  4  Test images and its ground truth map

    图  5  SARTNet总体架构

    HW分别代表特征图的高度和宽度

    Fig.  5  SARTNet overall architecture

    H and W represent the height and width of feature map, respectively

    图  6  ASE模块结构

    Fig.  6  ASE module structure

    图  7  模型损失曲线

    Fig.  7  Model loss curve

    图  8  赤潮信息提取的定性结果

    黄色矩形框表示明显的漏提或误提区域

    Fig.  8  Qualitative results of red tide information extraction

    The yellow rectangular box indicates obvious missed extraction or false extraction areas

    图  9  ASE模块输出特征图

    Fig.  9  ASE module output feature map

    图  10  多层次主干输出的定性结果

    Fig.  10  Qualitative results of multilayer backbone outputs

    图  11  赤潮水体和非赤潮水体的光谱曲线

    Fig.  11  Spectral curves of red tide water and normal water

    图  12  输入不同数据的定性结果

    Fig.  12  Qualitative results of experiments with different band settings

    图  13  赤潮探测模型适用性分析结果

    Fig.  13  Applicability analysis results of red ride detection model

    表  1  GF-1 WFV影像参数

    Tab.  1  GF-1 WFV image parameters

    参数 多光谱宽幅相机
    波长范围 0.45~0.52 μm
    0.52~0.59 μm
    0.63~0.69 μm
    0.77~0.89 μm
    空间分辨率 16 m
    幅宽 800 km
    重访周期 2 d
    下载: 导出CSV

    表  2  不同方法赤潮信息提取的定量结果

    Tab.  2  Quantitative results of red tide information extraction by different methods

    方法 精确率/% 召回率/% F1分数 参数量/M
    研究区I GF1_RI 67.64 80.77 0.73
    U-Net 84.32 91.32 0.87 31.02
    PSPNet 91.94 83.62 0.87 23.60
    DeepLabv3+ 88.41 85.41 0.86 12.04
    SARTNet 88.81 90.87 0.89 5.01
    研究区II GF1_RI 79.48 60.40 0.68
    U-Net 78.80 80.47 0.79 31.02
    PSPNet 84.01 83.91 0.83 23.60
    DeepLabv3+ 87.01 81.82 0.84 12.04
    SARTNet 91.09 87.29 0.89 5.01
    注:加粗数据代表同列数据最大值。
    下载: 导出CSV

    表  3  ASE模块消融实验结果

    Tab.  3  ASE module ablation experimental results

    ASE 精确率/% 召回率/% F1分数
    88.89 86.50 0.87
    90.01 89.08 0.89
    下载: 导出CSV

    表  4  不同层次主干对赤潮信息提取的定量结果

    Tab.  4  Quantitative results of red tide information extraction at different levels

    主干层次 精确率/% 召回率/% F1分数
    SARTNet-1 91.79 85.44 0.88
    SARTNet-2 90.01 89.08 0.89
    SARTNet-3 88.49 88.50 0.88
    下载: 导出CSV

    表  5  输入不同数据的定量结果

    Tab.  5  Quantitative results of experiments with different band settings

    波段设置 精确率/% 召回率/% F1分数
    4波段 88.81 90.87 0.89
    4波段+NDVI 87.08 60.45 0.71
    4波段+GF1_RI 87.54 91.65 0.89
    4波段+GF1_RI+NDVI 87.17 67.91 0.76
    下载: 导出CSV

    表  6  模型适用性分析实验结果

    Tab.  6  Experimental results of model applicability analysis

    影像 精确率/% 召回率/% F1分数
    GF-1 WFV3影像 91.37 86.70 0.89
    GF-1 WFV4影像 89.16 87.05 0.88
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-30
  • 修回日期:  2022-12-05
  • 网络出版日期:  2022-12-26
  • 刊出日期:  2023-07-01

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