基于地理加权回归模型的水深反演算法研究

doi:10.3969/j.issn.1001-909X.2018.04.005

海洋学研究 ›› 2018, Vol. 36 ›› Issue (4): 35-42.DOI: 10.3969/j.issn.1001-909X.2018.04.005

基于地理加权回归模型的水深反演算法研究

刘源^1,3, 邱振戈^1,3, 栾奎峰^*1,3, 侍炯^2,3, 朱卫东^1,3, 刘鲁燕^2,3, 沈蔚^1,3, 曹彬才^1,3

1.上海海洋大学海洋科学学院,上海 201306;
2.上海市海洋管理事务中心,上海 200050;
3.上海河口海洋测绘工程技术研究中心,上海 201306

收稿日期:2018-04-18 修回日期:2018-10-18 出版日期:2018-12-15 发布日期:2022-11-18
通讯作者: * 栾奎峰(1981-),男,讲师,主要从事激光雷达和海洋观测方面的研究。 E-mail:kfluan@shou.edu.cn
作者简介:刘源(1992-),男,河南驻马店市人,主要从事海洋遥感方面的研究。E-mail:liuyuan_1992@126.com
基金资助:
国家重点研发计划资助(2016YFC1400904);高分辨率对地观测系统重大专项资助(42-Y30B18-9001-15/17);上海市科委重点科研计划资助(17DZ1204902)

Research on water depth inversion algorithm based on Geographically Weighted Regression Model

LIU Yuan^1,3, QIU Zhen-ge^1,3, LUAN Kui-feng^*1,3, SHI Jiong^2,3, ZHU Wei-dong^1,3, LIU Lu-yan^2,3, SHEN Wei^1,3, CAO Bin-cai^1,3

1. College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China;
2. Shanghai Marine Affairs Management Center, Shanghai 200050, China;
3. Shanghai Engineering Research Center of Estuarine and Oceanographic Mapping, Shanghai 201306, China

Received:2018-04-18 Revised:2018-10-18 Online:2018-12-15 Published:2022-11-18

摘要/Abstract

摘要： 利用卫星多光谱数据反演浅海水深是水深测量的一种重要手段。已有水深反演方法是在研究区建立统一的数学参数的反演模型,未考虑由于海底底质和水质变化导致的空间非平稳性问题。本文使用地理加权回归模型(Geographically Weighted Regression, GWR)对回归参数在空间上进行估计,针对GWR模型的带宽对反演精度的影响,使用了交叉验证(Cross Validation,CV)的方法来确定最佳带宽,并以南海永兴岛和甘泉岛海域为实验区,基于WordiVew-2多光谱数据对使用GWR模型的可行性和精度进行了验证。实验结果表明:永兴岛研究区GWR模型精度较线性回归模型提高了36.05%,在0~5,5~10,10~15和15~20 m区间,精度分别提高了49.46%,39.97%,12.36%和49.68%;甘泉岛研究区GWR模型精度较线性回归模型提高了8.08%,在0~5,5~10,10~15和15~20 m区间,精度分别提高了12.05%,16.23%,4.49%和12.23%,表明GWR模型具有更好的水深反演效果。

关键词: GWR, 带宽, 多光谱遥感, 水深反演, 南海, WorldView-2

Abstract: Inversion of shallow seawater depth using satellite multi-spectral data is an important measure of water depth measurement. The existing water depth inversion method is to establish an inversion model of unified mathematical parameters in the study area, without considering the problem of spatial non-stationarity caused by changes in sea floor sediment and water quality. In this study, the Geographically Weighted Regression (GWR) model was used to estimate the regression parameters in space. For the influence of the bandwidth of the GWR model on the inversion accuracy, a Cross Validation (CV) method was used to determine the best bandwidth, taking the sea areas of Woody Island and Ganquan Island in the South China Sea as experimental areas, the feasibility and accuracy of the GWR model were verified based on WordView-2 multi-spectral data. As a result of the experiment, the accuracy of the GWR model in the study area of Woody Island was improved by 36.05% compared with the linear regression model, and in the ranges of 0-5 m, 5-10 m, 10-15 m, and 15-20 m, the precision was increased by 49.46%, 39.97%, 12.36% and 49.68% respectively. The precision of GWR model in the study area of Ganquan Island was improved by 8.08%. In the ranges of 0-5 m, 5-10 m, 10-15 m, and 15-20 m, compared with the linear regression model, the precision was improved by 12.05%, 16.23%, 4.49% and 12.23% respectively, indicating that the GWR model has a better water depth inversion performance.

Key words: GWR, bandwidth, multi-spectral remote sensing, water depth inversion, South China Sea, WorldView-2

中图分类号:

TP79

刘源, 邱振戈, 栾奎峰, 侍炯, 朱卫东, 刘鲁燕, 沈蔚, 曹彬才. 基于地理加权回归模型的水深反演算法研究[J]. 海洋学研究, 2018, 36(4): 35-42.

LIU Yuan, QIU Zhen-ge, LUAN Kui-feng, SHI Jiong, ZHU Wei-dong, LIU Lu-yan, SHEN Wei, CAO Bin-cai. Research on water depth inversion algorithm based on Geographically Weighted Regression Model[J]. Journal of Marine Sciences, 2018, 36(4): 35-42.

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基于地理加权回归模型的水深反演算法研究

Research on water depth inversion algorithm based on Geographically Weighted Regression Model

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