多尺度连续小波变换岸基高光谱溢油厚度定量监测

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Abstract Focusing on the monitoring of 0# diesel oil film thickness, the primary objective is to meticulously analyze the spectral curve characteristics of different thickness oil films. Secondly, to delve deeper into the intricate relationship between spectral data and oil film thickness, the Morlet multi-scale continuous wavelet transform (CWT) technique is introduced. This enables the precise identification of spectral bands that are highly sensitive to oil film thickness, effectively addressing the challenge posed by the high dimensionality and complexity of hyperspectral data. Consequently, this approach significantly enhances the accuracy of thickness regression predictions. At the same time, the CatBoost regression model, with its efficient computing performance, strong feature capture ability, and excellent generalization ability, efficiently integrates these sensitive features and constructs a precise regression prediction model of the oil film thickness, accelerating the real-time monitoring speed of oil spill events, thereby achieving the immediate capture of changes in the thickness of the oil film and ensuring the accuracy of the prediction results, providing a solid scientific basis and technical support for the rapid initiation of oil spill emergency responses and the formulation of precise prevention and control strategies. The results show that the multi-scale continuous wavelet transform technology plays a key role in this study. It can effectively extract the sensitive bands highly related to the thickness of the oil film from the massive hyperspectral data, thereby significantly improving the accuracy and efficiency of oil spill thickness monitoring. The CatBoost regression model can better capture the change category characteristic data of the oil film thickness, further enhancing the generalization ability and robustness of the model. The diesel oil film thickness prediction model established by the CatBoost regression model shows extremely high accuracy, with R²=0.90, RMSE=95.14μm,δ=30.126% on the validation set.

Key words： hyperspectral imaging spectral analysis continuous wavelet transform oil spill thickness quantitative monitoring

Received: 20 May 2024

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	Articles by authors
	DONG Yang
	CUI Hou-xin
	DENG Jia-chun
	MA Jun-jie
	QIN Hai-xiao

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DONG Yang,CUI Hou-xin,DENG Jia-chun等. Quantitative monitoring of oil spill thickness using multiscale continuous wavelet-based shore-based hyperspectral analysis[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(11): 6397-6407.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I11/6397

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