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HY-1C卫星CZI载荷的黄海绿潮提取研究

刘锦超 刘建强 丁静 陆应诚

刘锦超,刘建强,丁静,等. HY-1C卫星CZI载荷的黄海绿潮提取研究[J]. 海洋学报,2022,44(5):1–11 doi: 10.12284/hyxb2022097
引用本文: 刘锦超,刘建强,丁静,等. HY-1C卫星CZI载荷的黄海绿潮提取研究[J]. 海洋学报,2022,44(5):1–11 doi: 10.12284/hyxb2022097
Liu Jinchao,Liu Jianqiang,Ding Jing, et al. A refined imagery algorithm to extract green tide in the Yellow Sea from HY-1C satellite CZI measurements[J]. Haiyang Xuebao,2022, 44(5):1–11 doi: 10.12284/hyxb2022097
Citation: Liu Jinchao,Liu Jianqiang,Ding Jing, et al. A refined imagery algorithm to extract green tide in the Yellow Sea from HY-1C satellite CZI measurements[J]. Haiyang Xuebao,2022, 44(5):1–11 doi: 10.12284/hyxb2022097

HY-1C卫星CZI载荷的黄海绿潮提取研究

doi: 10.12284/hyxb2022097
基金项目: 河北省重点研发计划(21373301D);国家自然科学基金(42071387,41771376);南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项(GML2019ZD0302)。
详细信息
    作者简介:

    刘锦超(1997-),男,山东省济宁市人,研究方向为海洋环境光学遥感。E-mail: MG1927066@smail.nju.edu.cn

    通讯作者:

    陆应诚(1979-),男, 教授, 研究方向为海洋环境遥感。E-mail: Luyc@nju.edu.cn

  • 中图分类号: X834

A refined imagery algorithm to extract green tide in the Yellow Sea from HY-1C satellite CZI measurements

  • 摘要: 海洋一号C(HY-1C)卫星是中国首颗海洋水色业务卫星,其搭载的海岸带成像仪(CZI)在近海海洋环境监测中正发挥越来越重要的作用;随着搭载有相同传感器的HY-1D卫星发射,双星组网观测,可形成3天2次的高频次、大范围对海观测能力,在海洋漂浮藻类、海洋溢油等目标探测方面具备优异的效能。高空间分辨率光学数据中包含了丰富的海洋环境信息,给特定目标的识别提取带来一定干扰。本研究面向HY-1C卫星CZI载荷开展中国近海漂浮藻类识别提取的业务化应用需求,发展基于藻类缩放指数与虚拟基线高度融合的海洋漂浮藻类识别提取算法,算法优选适用于无短波红外波段国产数据的虚拟基线高度指数来增强藻类信号,通过藻类缩放指数滑动窗口运算,有效剔除高空间分辨率光学数据中的复杂干扰信息,实现了基于CZI数据的海洋漂浮藻类高精度提取,且具有较好的计算运行效率。此外,结合准同步高分卫星16 m多光谱数据,开展CZI数据含藻像元的不确定性分析,发现CZI数据反演结果对近海小斑块漂浮藻类存在不可忽视的高估现象。研究还指出,光学数据用于漂浮藻类监测,其不确定性不仅来源于传感器的空间分辨率差异,还与海洋漂浮藻类形态特征的空间分异性有关。明晰海洋漂浮藻类的形态学空间分异特征,将有助于提高光学数据反演结果的精度,并阐明不确定性。
  • 图  1  2019年黄海大型漂浮藻类标准假彩色影像

    HY-1C卫星CZI数据,R: 825 nm,G: 650 nm,B: 560 nm

    Fig.  1  Standard false color images of macroalgae in the Yellow Sea during 2019

    Data from HY-1C satellite CZI, R: 825 nm, G: 650 nm, B: 560 nm

    图  2  滑动窗口尺寸变化下的缩放藻类指数图像

    a. 研究区RGB合成图像(R: 825 nm, G: 650 nm, B: 560 nm);b, c, d中漂浮藻类斑块大小差异显著,其中红色框表示该区域的最佳滤波窗口尺寸

    Fig.  2  Scaled algae index images with changing size of sliding window

    a. Red-Green-Blue composite image by bands 4 (R, 825 nm), 3 (G, 650 nm) and 2 (B, 560 nm) in study area. Floating algal patches in b, c, d show significant differences in size. The red box in b, c and d indicate the optimal sliding window size for the region

    图  3  大型漂浮藻类虚拟基线高度法(VB-FAH)与对VB-FAH图像经过缩放藻类指数计算后值(SAI(VB))对比图

    a. 2019年6月8日HY-1C卫星CZI传感器RGB合成影像(R: 825 nm, G: 650 nm, B: 560 nm);b. 大型漂浮藻类虚拟基线高度法图;c. SAI (VB)指数图;d. VB-FAH垂直剖面;e. SAI (VB)垂直剖面

    Fig.  3  Comparison between virtual baseline floating macroalgae height (VB-FAH) and SAI (VB)

    a. HY-1C/CZI Red-Green-Blue composite image by bands 4 (R, 825 nm), 3 (G, 650 nm) and 2 (B, 560 nm) acquired on June 8, 2019; b. image of VB-FAH; c. image of SAI (VB); d. vertical profile of VB-FAH; e. vertical profile of SAI (VB)

    图  4  基于SAI(RED)的小云斑干扰信息的剔除

    Fig.  4  Elimination for confusion signal of cloud spots based on SAI (RED)

    图  5  海面目标误判信息剔除与修正结果

    Fig.  5  Results of elimination and correction for misclassification of sea surface targets

    图  6  基于HY-1C星CZI数据提取的2019年海面漂浮绿潮

    图中“面积”表示含藻像元面积

    Fig.  6  The information of floating green tides in 2019 extracted from HY-1C CZI measurements

    The “area” in the figure represents the area of algae-containing pixels

    图  7  感兴趣区域内人工解译结果与算法提取结果的对比分析

    Fig.  7  Comparative study between manual interpretation and algorithm extraction within the regions of interest

    图  8  2019年6月8日HY-1C/CZI和GF-1/WFV1准同步数据反演结果比较

    Fig.  8  Comparison of inversion results between image of HY-1C/CZI and quasi-synchronous image of GF-1/WFV1 on June 8, 2019

    表  1  精度评价

    Tab.  1  Accuracy evaluation

    区域编号人工解译/像元数算法提取/像元数λ/%
    1126 907125 6001.03
    2104 78297 0212.37
    394 96392 3452.76
    468 72274 4528.34
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-13
  • 修回日期:  2021-08-30
  • 网络出版日期:  2022-06-15
  • 刊出日期:  2022-06-15

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