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

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

Abstract

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

CLC Number:

TP79

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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