Application and comparison of machine learning algorithms in remote sensing water depth inversion
RAO Yali
SHEN Wei
LUAN Kuifeng
JI Qian
MENG Ran
HAO Lihua
Abstract：According to the principle of remote sensing water depth inversion,this paper used the WorldView-2 multi-spectral satellite remote sensing image and airborne Lidar data for four machine learning models,including extreme gradient boosting(XGBoost),support vector machine(SVM),kernel ridge regression(KRR)and least absolute shrinkage and selection operator(LASSO),to ex-plore the impact of different machine learning models on the accuracy of water depth inversion.For deepth data,when the training sample and the test sample were the same,the grid search method was used to find the optimal parameter combination of the machine learning algorithm,and the water depth inversion experiment was carried out on the shallow waters around Ganquan island.Through the com-parison and analysis of the inversion results of four types of machine learning models,the results were as follows:in the shallow water depth area of 20m,XGBoost model had strong learning ability,with correlation coefficient(R2)of 0.97,root mean square error(RMSE)of 0.85 m,average absolute error(MAE)of 0.63 m and average relative error(RME)of 19％,which was better than the other three machine learning models,and the overall effect was the best.It can be used to predict the water depth around Ganquan island and provide reference for subsequent water depth inversion research.
Keywords：remote sensing water depth inversionmachine learningXGBoost modelmultispectral image
Publication Date：2024-10-28
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 56-63 )