ABSTRACT Marine oil spills cause great pollution to the marine environment and require development of efficient cleaning plans. Accurate identification of the oil type involved in the spill is of great significance for rapid and effective treatment. Hyperspectral remote sensing plays an important role in oil spill detection and oil type identification. We designed an outdoor oil spill experiment to simulate an oil spill in a marine environment. Five common oil types were selected as the experimental starting materials: crude oil, fuel oil, diesel oil, gasoline, and palm oil. Hyperspectral data of the five oils were collected from different solar times by Analytical Spectral Devices (ASD) FieldSpec4. The relationship between the spectral absorption baseline height of the different oil types and solar time is investigated. The characteristic analysis method of spectral standard deviation was used to obtain characteristic bands of the different oil types. Using both full spectrum and selected characteristic bands, oil type identification experiments were performed using the Support Vector Machine (SVM) model, respectively. The results show that oil type identification using selected characteristic bands is 3.70% more accurate compared with that using the full spectrum, reaching 83.33%.

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基于高光谱遥感的常见海洋溢油类型特征分析与识别

摘要 海洋溢油对海洋环境造成严重污染，需要制定有效的清洁计划。准确识别溢油涉及的油类对快速有效处理具有重要意义。高光谱遥感在溢油检测和油类识别中发挥着重要作用。我们设计了一个室外漏油实验来模拟海洋环境中的漏油。选择五种常见的油类作为实验原料：原油、燃料油、柴油、汽油和棕榈油。五种油类的高光谱数据是通过分析光谱设备 (ASD) FieldSpec4 从不同的太阳时间收集的。研究了不同油类的光谱吸收基线高度与太阳时之间的关系。采用光谱标准差特征分析方法获得不同油类的特征谱带。使用全光谱和选定的特征带，分别使用支持向量机 (SVM) 模型进行油类型识别实验。结果表明，选择特征谱带进行油品类型识别比全谱识别准确率提高3.70%，达到83.33%。