Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning

Wang Xin; Bei Yixuan; Chen Zhuo; Zhang Kai

doi:10.12284/hyxb2022033

Volume 44 Issue 11

Nov. 2022

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Wang Xin，Bei Yixuan，Chen Zhuo, et al. Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning[J]. Haiyang Xuebao，2022, 44(11)：159–169 doi: 10.12284/hyxb2022033

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Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning

doi: 10.12284/hyxb2022033

Wang Xin^{1, 4
,},
Bei Yixuan¹,
Chen Zhuo³,
Zhang Kai^{1, 2
,
,}

1.
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
2.
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
3.
China Ordnance Industry Survey and Geotechnical Institute Co., Ltd., Beijing 100053, China
4.
Guangzhou Sanhai Marine Engineering Surveying and Designing Co., Ltd., Guangzhou 510220, China

Received Date: 2021-10-11
Rev Recd Date: 2021-12-03

Available Online: 2022-08-30

Publish Date: 2022-11-03

Abstract

Abstract

Retrieving shallow water depth based on multispectral satellite imagery is highly cost-effective. However, the extensive application of satellite-derived bathymetry has been restricted by its low prediction accuracy. To improve about the accuracy of the retrieved bathymetry, spatial autocorrelation features within the in situ depth measurements and the multi-spectral image are focused in this research. To this end, we develop a machine learning method combining with spatial autocorrelation features and statistical intercorrelation features of learned samples. The experimental results of Xisha Beidao show that compared with the traditional machine learning, the accuracy of the new method is improved by 18% when the number of in situ depths is small. On the contrary, when the number of in situ depths is large, an improvement of 27% in root mean square error is achieved. This demonstrates that incorporating the spatial autocorrelation features of data sources into the machine learning can significantly improve the prediction accuracy, and then provide effective data support for shallow ocean research.
- bathymetry retrieval,
- random forest,
- machine learning,
- spatial autocorrelation

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References(38)

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