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A spectral-spatial approach for detection of single-point natural gas leakage using hyperspectral imaging
International Journal of Greenhouse Gas Control ( IF 3.9 ) Pub Date : 2020-11-06 , DOI: 10.1016/j.ijggc.2020.103181
Jinbao Jiang , Weiwei Ran , Kangni Xiong , Yingyang Pan

Recent studies have shown that underground natural gas storage leaks can be indirectly detected through the spectral changes of surface vegetation. However, due to the phenomenon of different samples demonstrating the same spectrum, using a spectral-based approach may result in misdetection. Vegetation stressed by natural gas leakage has unique spatial patterns. Therefore, a field experiment of natural gas leakage vegetation stress was carried out. Hyperspectral images of bean, corn crops, and grasslands were obtained, which led to a proposed new spectral-spatial based methodology to detect natural gas leaks and areas of vegetation stress. First, the vegetation indices and the color index were extracted, then respectively segmented using the Otsu and the proposed threshold segmentation methods. Next, the shape parameters of the posture ratio and rectangularity of the segmented objects were used to construct a circular detection model. The accuracies of the detection results based on the vegetation indices and color index were 53 % and 56 %, respectively. Finally, based on the concentric ring spatial distribution pattern of the stress zones, the two types of detection results were fused using the linearly weighted fusion method, after which all the leakage points were accurately detected and localized, without any false alarms.



中文翻译:

使用高光谱成像技术检测单点天然气泄漏的光谱空间方法

最近的研究表明,可以通过地表植被的光谱变化间接检测地下天然气储存泄漏。但是,由于不同样品表现出相同光谱的现象,使用基于光谱的方法可能会导致检测错误。天然气泄漏引起的植被具有独特的空间格局。因此,进行了天然气泄漏植被应力的野外试验。获得了豆类,玉米作物和草原的高光谱图像,这导致提出了一种新的基于光谱空间的方法来检测天然气泄漏和植被胁迫区域。首先,提取植被指数和颜色指数,然后分别使用大津和提出的阈值分割方法进行分割。下一个,利用姿态比的形状参数和被分割物体的矩形来构建圆形检测模型。基于植被指数和颜色指数的检测结果的准确度分别为53%和56%。最后,根据应力带的同心环空间分布规律,采用线性加权融合方法对两种类型的检测结果进行融合,然后对所有泄漏点进行准确检测和定位,而不会产生任何误报。

更新日期:2020-11-06
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