An interval band selection method based on class saliency map to identify vegetation under natural gas microleakage stress

When microleakage occurs in underground natural gas pipelines, the surface gas concentration may not reach detectable thresholds. Hyperspectral remote sensing can monitor indirectly through the spectral changes of surface vegetation. However, hyperspectral data often have band redundancy, and existing band selection methods lack consideration for vegetation-specific spectral response characteristics and interpretability. In this study, an interval band selection method based on class saliency map (IBS-CSA) was proposed to extract characteristic bands for identifying vegetation under natural gas microleakage stress. Firstly, a field experiment was designed to investigate the impact of natural gas stress on grass, bean, and wheat. Then, an one-dimensional convolutional neural network was constructed to obtain class saliency maps for different stress levels. Based on these maps, a band importance factor was created, and characteristic bands were extracted by applying the delineated spectral intervals. Finally, the performance of the IBS-CSA method was validated through statistical analysis and comparative modeling. The results indicate that when the proportion of selected bands reaches 10% (equivalent to 10 bands), the characteristic bands selected by the IBS-CSA method perform excellent separability, with JM distances larger than 1.8 between healthy and stressed vegetation. Furthermore, compared to other band selection methods, the IBS-CSA method performs superior accuracy and stability across all classification models, with overall accuracies of 91.40%, 96.67%, and 96.67% on the grass, bean, and wheat test datasets, respectively. This study demonstrates the potential of IBS-CSA in extracting vegetation spectral features under natural gas microleakage stress, providing unique insights into the extraction of characteristic wavelengths of stressed vegetation.