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Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning
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Xin Wang1, 4, Yixuan Bei1, Zhuo Chen3, Kai Zhang1, 2, *
Haiyang Xuebao | 2022, 44(11) : 159 - 169
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Haiyang Xuebao | 2022, 44(11): 159-169
• Article •
Retrieving shallow bathymetry by integrating spatial autocorrelation features with machine learning
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Xin Wang1, 4, Yixuan Bei1, Zhuo Chen3, Kai Zhang1, 2, *
Affiliations
  • 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
Published: 2022-11-01 doi: 10.12284/hyxb2022033
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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
Xin Wang, Yixuan Bei, Zhuo Chen, Kai Zhang. 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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Article Info
doi: 10.12284/hyxb2022033
  • Receive Date：2021-10-11
  • Online Date：2026-02-01
  • Published：2022-11-01
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History
  • Received：2021-10-11
  • Revised：2021-12-03
Funding
Affiliations
    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
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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