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顾及空间自相关特征的机器学习水深反演方法研究

王鑫 贝祎轩 陈卓 张凯

王鑫,贝祎轩,陈卓,等. 顾及空间自相关特征的机器学习水深反演方法研究[J]. 海洋学报,2022,44(11):159–169 doi: 10.12284/hyxb2022033
引用本文: 王鑫,贝祎轩,陈卓,等. 顾及空间自相关特征的机器学习水深反演方法研究[J]. 海洋学报,2022,44(11):159–169 doi: 10.12284/hyxb2022033
Wang Xin,Bei Yixuan,Chen Zhuo, et al. Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning[J]. Haiyang Xuebao,2022, 44(11):159–169 doi: 10.12284/hyxb2022033
Citation: Wang Xin,Bei Yixuan,Chen Zhuo, et al. Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning[J]. Haiyang Xuebao,2022, 44(11):159–169 doi: 10.12284/hyxb2022033

顾及空间自相关特征的机器学习水深反演方法研究

doi: 10.12284/hyxb2022033
基金项目: 山东省自然科学基金(ZR2020MD084);国家自然科学基金重点基金(41930535)。
详细信息
    作者简介:

    王鑫(1996-),男,江苏省扬州市人,主要从事海洋水深反演研究。E-mail:wx849445406@qq.com

    通讯作者:

    张凯(1983-),男,副教授,主要从事海洋测绘相关研究。E-mail:zk0773@163.com

  • 中图分类号: TP79

Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning

  • 摘要: 基于多光谱影像的水深反演方法是获取近岸水深信息的高效手段,然而反演精度低一直是制约其广泛应用的瓶颈。本文聚焦于实测水深与多光谱数据自身的空间自相关特性,提出在机器学习框架下将学习样本的空间自相关特征与统计互相关特征相结合,以提高水深反演精度。西沙北岛海域的实验结果表明:在实测数据量较小的情况下,相比传统机器学习,顾及自相关特征的新方法可获得18%的精度提升;而当实测数据量充足时,精度提升可达到27%。结果表明,将数据源的空间自相关特征融入机器学习算法中,可显著提升多光谱水深反演结果的精确性,进而为浅海海洋研究提供有效数据支撑。
  • 图  1  随机森林算法示意图

    Fig.  1  Schematic diagram of random forest

    图  2  空间自相关随机森林算法示意图

    Fig.  2  Schematic diagram of spatial autocorrelation random forest

    图  3  北岛地理位置(a)及原位水深点测量分布(b)

    Fig.  3  Location of Beidao (a) and distribution of in situ depth measurements (b)

    图  4  水深点分布区间与数量

    Fig.  4  Distribution interval and number of water depth points

    图  5  全局的全局莫兰指数及半方差的变化

    Fig.  5  Global Moran’s I and semivariance with different lags of variable

    图  6  对数比值(a)、普通克里金(b)、随机森林(c)和空间自相关随机森林(d)模型反演水深散点图(训练数量为150)

    Fig.  6  Scatter diagram of predicted depth values by Stumpf (a), ordinary Kriging (b), random forest (c), and spatial autocorrelation random forest (d) models (the number of training data points is 150)

    图  7  对数比值(a)、普通克里金(b)、随机森林(c)和空间自相关随机森林(d)模型的残差散点图(训练数量为150)

    Fig.  7  Scatter diagram of residual error by Stumpf (a), ordinary Kriging (b), random forest (c), and spatial autocorrelation random forest (d) models (the number of training data points is 150)

    图  8  训练样本量500时残差分布直方图(a)和不同训练数据占比下的均方根误差(b)

    Fig.  8  The histogram of error distribution when the training sample is 500 (a) and root mean square error for different training data shares (b)

    图  9  反演的陆域与水下地形图(a)和地貌细节图(b)

    a图分辨率为2 m;b图左列为卫星影像,右列为反演的海底地形

    Fig.  9  Bathymetry retrieval of onshore and inversion bathymetric topographic map (a) and geomorphic details (b)

    The resolution is 2 m in a; the left are the satellite images and the right are the retrieved bathymetric topographies in b

    表  1  研究区变量的全局莫兰指数、Z值和P

    Tab.  1  Global Moran’s I, normalized Z value and P value of variables in the study area

    环境变量莫兰指数ZP
    水深0.5118.8530.001
    蓝光波段0.63110.9670.001
    绿光波段0.5018.9540.001
    红光波段0.3015.5310.001
    下载: 导出CSV

    表  2  验证数据测试精度对比

    Tab.  2  Accuracy comparison of different methods

    方法均方根误差/m平均绝对误差/m决定系数
    对数比值模型2.0671.6080.797
    普通克里金模型1.8941.4870.845
    随机森林模型1.6351.0580.888
    空间自相关随机森林模型1.3380.9980.923
    下载: 导出CSV
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  • 收稿日期:  2021-10-11
  • 修回日期:  2021-12-03
  • 网络出版日期:  2022-08-30
  • 刊出日期:  2022-11-03

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