Hyperspectral image methane detection based on DETR

As a potent greenhouse gas, methane (CH₄) leakage not only intensifies global warming, but also brings huge security risks. In recent years, with the rapid development of remote sensing technology, methane leakage detection using hyperspectral images has become a research hotspot. Among them, the airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG) shows great potential in methane leakage detection with its high spectral resolution and wide spectral coverage. However, due to the complexity of land type conditions and the interference of confounding factors similar to the spectral characteristics of methane, the existing analytical methods are often difficult to obtain ideal detection results. Therefore, based on the DETR model, a novel end-to-end spectral absorption wavelength sensing transformer target detection network is proposed to improve the accuracy and efficiency of methane gas leakage detection. The reference spectrum feature generator and query refiner modules together improve the design of traditional transformer. Based on the traditional linear filter, a new spectral linear filter is proposed, which can better whiten the background distribution and amplify the methane signal by strategically selecting the relevant pixels in the spectral domain. The method presented in this paper has certain significance for improving the detection of methane gas leakage.