基于光谱曲线响应特性的油膜厚度估计模型分析

如今，海上溢油事故频发，如何对溢油的油量进行估计，是一个重要课题。如果可以得到溢油量，那么对后续的处理以及损失的评估都会有较大的帮助。高光谱遥感技术的快速发展使对油膜厚度的定量估计成为可能。采用AvaSpec光谱仪测量不同厚度的油膜，然后对得到的光谱曲线的多种曲线特征进行提取，分析其与油膜厚度之间的关系。结果表明，油膜厚度与基于高光谱位置变量的Rg和Ro、三角植被指数的RDVI和TVI以及Haboudane关系式相关性较大。分别采用曲线拟合、BP神经网络和基于SVD的迭代方法建立油膜曲线特征与油膜厚度之间的预测关系，并以此对不同的油膜光谱曲线进行油膜厚度估计，对得到的结果进行精度检测和运行时间分析，最终得出对每个估计模型的分析评价。

Study of Prediction Models for Oil Thickness Based on Spectral Curve

Nowdays, oil spill accidents on sea occur frequently. It is a practical topic to estimate the amount of spilled oil, which is helpful for the subsequent processing and loss assessment. With the rapid development of hyperspectral remote sensing technology, estimating the oil thickness becomes possible. Firstly, a series of oil thicknesses are tested with the AvaSpec Spectrometer to get their corresponding spectral curves. And then the characteristics of the spectral curve are extracted to analyze their relationship with the oil thickness. The study shows that the oil thickness has large correlation with variables based on hyperspectral positions such as R_g, R_o, and vegetation indexes such as RDVI, TVI and Haboudane. Curve fitting, BP neural network and SVD iteration method were chosen to build the prediction models for oil thicknesses. Finally, the analysis and evaluation of each estimating model are provided.