Inversion of the Thickness of Crude Oil Film Based on an OG-CNN Model

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ID: 111744
2020
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Abstract
In recent years, marine oil spill accidents have occurred frequently, seriously endangering marine ecological security. It is highly important to protect the marine ecological environment by carrying out research on the estimation of sea oil spills based on remote sensing technology. In this paper, we combine deep learning with remote sensing technology and propose an oil thickness inversion generative adversarial and convolutional neural network (OG-CNN) model for oil spill emergency monitoring. The model consists of a self-expanding module for the oil film spectral feature data and an oil film thickness inversion module. The feature data self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured spectral data and then expand the number of samples using a generative adversarial network (GAN) to enhance the generalization of the model. The oil film thickness inversion module is based on a one-dimensional convolutional neural network (1D-CNN). It extracts the characteristics of the spectral feature data of oil film with different thicknesses, and then accurately inverts the oil film’s absolute thickness. In this study, emulsification was not a factor considered, the results show that the absolute oil thickness inversion accuracy of the OG-CNN model proposed in this paper can reach 98.12%, the coefficient of determination can reach 0.987, and the mean deviation remains within ±0.06% under controlled experimental conditions. In the model stability test, the model maintains relatively stable inversion results under the interference of random Gaussian noise. The accuracy of the oil film thickness inversion result remains above 96%, the coefficient of determination can reach 0.973, and the mean deviation is controlled within ±0.6%, which indicates excellent robustness.
Reference Key
jiang2020journalinversion Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zongchen Jiang;Yi Ma;Junfang Yang;Jiang, Zongchen;Ma, Yi;Yang, Junfang;
Journal journal of marine science and engineering
Year 2020
DOI
10.3390/jmse8090653
URL
https://www.mdpi.com/2077-1312/8/9/653
Keywords
Deep learning remote sensing gan crude oil film absolute thickness inversion 1d-cnn

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