Bathymetric inversion model for fusion of heterogeneous satellite remote sensing data

doi:10.13474/j.cnki.11-2246.2024.0504

Abstract

Abstract: To investigate the impact of image resolution and bathymetry models on the fusion of heterogeneous satellite data for depth inversion, this study integrates ICESat-2 laser altimetry data with multi-temporal Landsat 8, Sentinel-2, and WorldView-3 satellite data. Depth inversion is performed using logarithmic ratio model, multi-band model, BP neural network, support vector machine, random forest, and extreme gradient boosting. Experimental results show that the spatial resolution of the images has an insignificant effect on the accuracy of depth inversion. Considering both the accuracy and resolution of the inversion results, Sentinel-2 satellite data performs the best. Moreover, the XGBoost model outperforms other models in terms of inversion performance, achieving an optimal RMSE of 0.51 meters in the Dongsha Atoll experimental area. These results provide valuable reference for coastal depth measurement based on the fusion of heterogeneous remote sensing satellite data.

Key words: water depth retrieval, multispectral satellite remote sensing imagery, ICESat-2, data fusion, contrastive analysis

CLC Number:

P237

GUO Songtao, XING Shuai, ZHANG Guoping, KONG Ruiyao, CHEN Li. Bathymetric inversion model for fusion of heterogeneous satellite remote sensing data[J]. Bulletin of Surveying and Mapping, 2024, 0(5): 19-23.

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