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Deep learning for inversion of significant wave height based on actual sea surface backscattering coefficient model

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Abstract

Ocean waves are complex systems with the contributions of wind waves and swells. The study on interaction mechanism between electromagnetic wave and actual sea surface is of significant importance in ocean remote sensing and engineering application, which is also helpful in the prediction and inversion of wave information. In this paper, an efficient model for estimating backscattering coefficient is built, considering the characteristics of the wind-wave regime based on the inverse wave age. The backscattering coefficient results have been verified by comparing with the data collected in Lingshan Island during the period of October and November 2014 at low grazing angles and the Ku-band measurements at moderate grazing angles. The results indicate perfect agreement (within about 2 dB) with field data. Deep learning is an excellent method that can be used not only for classification but also for inversion and fitting of non-linear functions. In order to simulate the application of actual radar detection and inversion technology, the inversion of significant wave height from actual sea surface backscattering coefficients train data sets has been performed by using deep learning technology. The accuracy of 99.01% has been achieved under the condition of three hidden layers and iterating 100 times. The root mean square errors of the test data sets are less than 0.10, which indicates that deep learning is available in the inversion of significant wave height.

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Acknowledgments

The authors would like to express their sincere thanks to the members of the clutter research group in China Research Institute of Radiowave Propagation for their work on the sea clutter experiment. And the ocean environment parameters data have been kindly provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), (https://www.ecmwf.int). This research was alos funded by the National Natural Science Foundation of China (No. 61571355 and No. 61601355), and the Pre-Research Foundation of China (No. FY1502802001).

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Authors and Affiliations

  1. School of Physics and Optoelectronic Engineering, Xidian University, Xi’an, 710071, China

    Tao Wu, Yun-Hua Cao & Zhen-Sen Wu

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jia-Ji Wu & Tan Qu

  3. China Research Institute of Radiowave Propagation, Qingdao, 266107, China

    Jin-Peng Zhang

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  1. Tao Wu
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  2. Yun-Hua Cao
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  4. Jia-Ji Wu
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Correspondence to Yun-Hua Cao.

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Wu, T., Cao, YH., Wu, ZS. et al. Deep learning for inversion of significant wave height based on actual sea surface backscattering coefficient model. Multimed Tools Appl 79, 34173–34193 (2020). https://doi.org/10.1007/s11042-019-07967-6

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