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Oil Slope Index: An algorithm for crude oil spill detection with imaging spectroscopy

机译:斜率指数:一种使用成像光谱技术检测原油泄漏的算法

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Marine oil spill is a major threat to marine and coastal ecosystems and is seen relatively often, such as the Deepwater Horizon oil spill disaster in the Gulf of Mexico in 2010 and Bohai Sea oil spills in China in 2011. Fast and accurate discrimination of oil spill is the largest challenge in detection of oil spills using remote sensing technology. In this research imaging spectroscopic analysis and Oil Slope Index(OSI) were developed to map the locations of surface crude oil in Gulf of Mexico using the SpecTIR data which was collected at 2.2m GSD and 360 spectral channels, covering 390–2450nm. The spectral features and differences of the main objects of oil, sea water and clouds can be found in the DN value of pixel spectra. The slope difference in the range from 550nm to 750nm between crude oil and other objects can be taken as a key feature for detection of crude oil on the sea surface. The Oil Slope Index(OSI) avoids the absorption bands of O2 and H2O in the air and transforms the imaging spectroscopy data into a single band image that shows the distribution of crude oil spill. OSI values can be easily calculated from radiance or DN data and no additional pre-processing of the imagery was necessary before crude oil detection. The result shows that the algorithms work well for oil spill detection which integrated the spectral feature of oil, sea water and clouds by establishing a decision tree. The automatic determination of thresholds by applying Otsu's image segmentation can realize the fast and automatic extraction of surface crude oil. This study demonstrated that the Oil Slope Index (OSI) has the potential to become a useful image processing algorithm and operational tool for imaging spectroscopy detection of crude oil spill.
机译:海洋漏油是对海洋和沿海生态系统的主要威胁,并且相对频繁地看到,例如2010年墨西哥湾的Deepwater Horizo​​n漏油灾难和2011年中国的渤海漏油事件。快速,准确地识别漏油使用遥感技术检测漏油是最大的挑战。在这项研究中,使用SpecTIR数据(在2.2m GSD和360光谱通道(覆盖390-2450nm)收集),开发了成像光谱分析和油坡指数(OSI)以绘制墨西哥湾地表原油的位置。在像素光谱的DN值中可以发现石油,海水和云的主要物体的光谱特征和差异。原油与其他物体之间在550nm至750nm范围内的斜率差异可以作为检测海面原油的关键特征。斜率指数(OSI)避免了空气中O2和H2O的吸收带,并将成像光谱数据转换为显示原油泄漏分布的单带图像。 OSI值可以很容易地从辐射率或DN数据计算得出,并且在检测原油之前不需要对图像进行额外的预处理。结果表明,该算法通过建立决策树融合了油,海水和云层的光谱特征,对于漏油检测效果很好。通过使用大津的图像分割自动确定阈值,可以实现表面原油的快速,自动提取。这项研究表明,油边坡指数(OSI)有可能成为有用的图像处理算法和用于原油泄漏的成像光谱检测的操作工具。

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