基于自扩展深度置信网络的原油油膜绝对厚度反演研究

海上溢油事故危害海洋生态安全、人类健康与经济发展.开展基于遥感技术的海面溢油量的估算研究,对于保护海洋生态环境具有重要意义,而油膜厚度反演是溢油量估算的核心环节.本文通过搭建室外实验场景,模拟真实海面溢油环境,基于现场实测高光谱遥感数据,开展海面溢油厚度反演研究.本文将深度学习与遥感技术融合,提出了基于自扩展深度置信网...

Inversion of absolute thickness of crude-oil film based on self-expanding deep belief network

Marine oil spills seriously endanger marine ecological security,human health,and economic development.Thus,to protect the marine ecological environment,it is very important to carry out research into the estimation of marine oil spillages using remote-sensing technology.The thickness inversion of an oil film is the key indicator for the estimation of oil spill volume.We set up an outdoor experimental site to simulate real oil spills on a sea surface and,by using field-measured hyperspectral remote-sensing data,we study the thickness inversion of sea oil spills.In this paper,we combine deep learning with remote-sensing technology and propose an oil-thickness-inversion generative adversarial and deep belief network(OG-DBN)model.The model consists of a self-expanding module for the oil film’s spectral feature data and a module for the oil-film thickness inversion.The self-expanding module can automatically select spectral feature intervals with good spectral separability based on the measured hyperspectral remote-sensing 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 DBN.It extracts the characteristics of the spectral feature data of oil films with different thicknesses,and then accurately inverts the oil film’s absolute thickness.The results show that the accuracy of absolute oil thickness inversion for the OG-DBN model proposed in this paper can reach 97.69%,the determination coefficient(R2)can reach 0.980,and the mean deviation remains within±0.06%under controlled experimental conditions.In the model stability test,the model maintains relatively stable inversion results.The accuracy of the oil-film thickness inversion remains above 93.33%,R2 remains above 0.957,and the mean deviation is controlled within±0.6%,which indicates excellent robustness.