Investigation on an inversion method of ocean salinity by lidar based on a neural network

Bao, Dong; Hua, Dengxin; Qi, Hao; Wang, Jun

doi:10.1016/j.optlaseng.2022.107354

Cited by 2 publications

(2 citation statements)

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“…The Adam [56] and Backpropagation algorithms [57,58] were used as the training algorithms, as shown below: The learnable parameter α, which is updated continually throughout the training phase, is the slope of the negative semi-axis of PRelu. Additionally, PReLU permits a group of neurons to share an α as well as allowing various neurons to have a distinct α.…”

Section: Deep Neural Network Modelmentioning

confidence: 99%

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The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Wen,

Zhang,

Shu

et al. 2023

Remote Sensing

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The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.

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Section: Deep Neural Network Modelmentioning

confidence: 99%

“…The Adam [56] and Backpropagation algorithms [57,58] were used as the training algorithms, as shown below:…”

Section: Deep Neural Network Modelmentioning

confidence: 99%