Application of CatBoost model in water depth inversion

doi:10.13474/j.cnki.11-2246.2022.0199

Abstract

Abstract: In multispectral remote sensing water depth inversion research, the traditional water depth inversion models have some limitations due to many factors affecting the accuracy of water depth inversion. Machine learning algorithms are more advantageous in solving nonlinear and highly complex problems, and their application in some specific areas of water depth inversion can improve the inversion accuracy. In this paper, using Sentinel-2 multispectral remote sensing images and LiDAR bathymetry data, it constructs CatBoost water depth inversion model with Oahu as the study area and compares the inversion accuracy with traditional water depth inversion models as well as XGBoost and LightGBM models in Boosting. It's showed in the experimental results that R-Square, root mean square error, mean absolute error, and mean relative error of the tuned CatBoost water depth inversion model are 96.19%, 1.09 m, 0.77 m and 9.61%, and the accuracy of the model is the highest, and the effect is more better.

Key words: water depth inversion, multispectral remote sensing, Sentinel-2, machine learning, CatBoost model

CLC Number:

P237

KONG Ruiyao, XIE Tao, MA Ming, KONG Ruilin. Application of CatBoost model in water depth inversion[J]. Bulletin of Surveying and Mapping, 2022, 0(7): 33-37.

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