Underground Natural Gas Microleakage Detection With Hyperspectral Imagery Based on Temporal Features and Ensemble Learning

Microleakage in underground natural gas storage has serious impacts on the environment or public safety. Recent studies have shown that hyperspectral imagery can detect natural gas microleakage by spectral or spatial features of vegetation indirectly. However, the identification of natural gas microleakage based on hyperspectral imagery still suffers from the following problems: the spectral and spatial features of vegetation change in a complex way with increasing stress time; the effectiveness of ensemble classifiers in recognizing natural gas-stressed vegetation in hyperspectral imagery is unclear; and there is also a lack of studies on the spatial and temporal changes of vegetation stress in natural gas microleakage. Therefore, hyperspectral images of wheat, bean, and grass in different periods were collected. First, the spectral features were filtered using the Relief-F algorithm. The spatial texture features were extracted using the grayscale co-occurrence matrix. The temporal features were extracted using the bi-temporal band ratio. Then, an ensemble classification model fusing spectral, spatial, and temporal features was established. Finally, the natural gas microleakage information was extracted based on the minimum external circle, and the spatial-temporal changes of vegetation stress were analyzed. The results showed that the average stress radii of wheat, bean, and grass were 1.07, 0.83, and 0.86 m, respectively. The mean absolute localization error of natural gas microleakage points was less than 0.4 m. This study provides a theoretical basis and technical support for the future use of satellite hyperspectral detection of microleakage in underground gas storage reservoirs.