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Crude oil, petroleum product, and water discrimination on terrestrial substrates with airborne imaging spectroscopy

机译:机载成像光谱法鉴别地面基质上的原油,石油产品和水

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The Deepwater Horizon explosion and subsequent sinking produced the largest oil spill in U.S. history. One of the most prominent portions of the response is mapping the extent to which oil has reached thousands of miles of shoreline. The most common method of detecting oil remains visual spotting from airframes, supplemented by panchromatic / multispectral aerial photography and satellite imagery. While this imagery provides a synoptic view, it is often ambiguous in its ability to discriminate water from hydrocarbon materials. By employing spectral libraries for material identification and discrimination, imaging spectroscopy supplements traditional imaging techniques by providing specific criteria for more accurate petroleum detection and discrimination from water on terrestrial backgrounds. This paper applies a new hydrocarbon-substrate spectral library to SpecTIR HST-3 airborne imaging spectroscopy data from the Hurricane Katrina disaster in 2005. Using common material identification algorithms, this preliminary analysis demonstrates the applicability and limitations of hyperspectral data to petroleum/water discrimination in certain conditions. The current work is also the first application of the petroleum-substrate library to imaging spectroscopy data and shows potential for monitoring long term impacts of Deepwater Horizon.
机译:深水地平线爆炸和随后的沉没造成了美国历史上最大的漏油事件。应对措施中最突出的部分之一是绘制石油已经到达数千英里海岸线的程度。检测机油的最常用方法仍然是从机身上目视观察,并辅以全色/多光谱航拍和卫星图像。虽然此图像提供了一个摘要视图,但它在区分水和碳氢化合物材料的能力上通常是模棱两可的。通过使用光谱库进行材料识别和鉴别,成像光谱学通过提供更准确的石油检测和地面背景上水的鉴别标准来补充传统的成像技术。本文将新的碳氢化合物底物谱库应用于2005年卡特里娜飓风灾难的SpecTIR HST-3机载成像光谱数据。使用常见的材料识别算法,该初步分析证明了高光谱数据在石油/水识别中的适用性和局限性。一定条件下。当前的工作也是石油底物库在成像光谱数据上的首次应用,并显示了监测“深水地平线”长期影响的潜力。

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