Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

Jinjin Wang; Yi Ma; Jingyu Zhang

doi:10.1117/12.2285344

8 March 2018 Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

Jinjin Wang, Yi Ma, Jingyu Zhang

Proceedings Volume 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 106111B (2018) https://doi.org/10.1117/12.2285344
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out.

The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2%、1.9%、3.4%、1.8%, and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

Citation Download Citation

Jinjin Wang, Yi Ma, and Jingyu Zhang "Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection", Proc. SPIE 10611, MIPPR 2017: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 106111B (8 March 2018); https://doi.org/10.1117/12.2285344

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