A multi-temporal method for detection of underground natural gas leakage using hyperspectral imaging

Abstract

Hyperspectral remote sensing images can indirectly detect underground natural gas leakage through the spectral and spatial variation of surface vegetation. However, due to the complexity of surface environment and the phenomenon of "different samples demonstrating the same spectrum", using a spectral-spatial based method may result in misidentification. The spectral and spatial characteristics of surface vegetation caused by natural gas micro-leakage will change with the increase of stress time and the growth of vegetation. Therefore, a field simulation experiment of natural gas micro-leakage vegetation stress was set up. Multi-temporal hyperspectral images of bean, corn and grassland were obtained, and a new spectral-spatial-temporal based methodology was proposed to identify natural gas micro-leakage points and stressed vegetation areas. First, multi-scale segmentation and rotation forest classification algorithm were used to conduct a spectral-spatial based classification for natural gas leakage stressed vegetation on each phase of the image. The precision and recall rate of the leak point detection results were 93% and 88%, respectively. Then, the classification and recognition result images of different time phases on the same plot were weighted stacked to obtain a spectral-spatial-temporal features fusion image. Finally, the fusion image was used to construct a spectral-spatial-temporal features fused recognition model to determine the final natural gas micro-leakage stress vegetation areas and the locations of suspected leak points. The precision and recall rate of final detection results were both 100%.