Abstract: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 Rg, Ro, 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.
Key words:Hyperspectral remote sensing;Feature extraction;Oil thickness;Estimate model
孙 鹏,宋梅萍*,安居白 . 基于光谱曲线响应特性的油膜厚度估计模型分析 [J]. 光谱学与光谱分析, 2013, 33(07): 1881-1885.
SUN Peng, SONG Mei-ping*, AN Ju-bai . Study of Prediction Models for Oil Thickness Based on Spectral Curve . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(07): 1881-1885.
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