Improved bathymetry retrieval based on multispectral image segmentation

Liyong Zhang; Yanru Wang; Qi Zhang; Xin Wang; Kai Zhang

doi:10.1117/12.3019500

23 January 2024 Improved bathymetry retrieval based on multispectral image segmentation

Liyong Zhang, Yanru Wang, Qi Zhang, Xin Wang, Kai Zhang

Proceedings Volume 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023); 129781L (2024) https://doi.org/10.1117/12.3019500
Event: 2023 4th International Conference on Geology, Mapping and Remote Sensing (ICGMRS 2023), 2023, wuhan, China

Abstract

Bathymetric inversion using multispectral imagery is an effective way to obtain shallow bathymetric information in water but is with relatively low accuracy. This study focuses on the solid disturbance of shallow seafloor substrate variation and proposes an image-segmentation-based method to improve the shallow bathymetry retrieval accuracy. The image is partitioned into different subregions with homogenous substrate properties, and the bathymetric inversion model is constructed separately in each subregion, thus improving the retrieval accuracy. Experimental results of the Ganquan Island region show that the accuracy of the bathymetric inversion was enhanced by 58.1% after image segmentation using muti statistic features.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liyong Zhang, Yanru Wang, Qi Zhang, Xin Wang, and Kai Zhang "Improved bathymetry retrieval based on multispectral image segmentation", Proc. SPIE 12978, Fourth International Conference on Geology, Mapping, and Remote Sensing (ICGMRS 2023), 129781L (23 January 2024); https://doi.org/10.1117/12.3019500

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KEYWORDS

Image classification

Image segmentation

Analytic models

Remote sensing

Multispectral imaging

Ocean optics

Water

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