Gas Plume Detection from Infrared Hyperspectral Remote Sensing Data Based on Deep Learning Algorithms

Infrared hyperspectral remote sensing is effective for chemical gas detection because gas molecules exhibit characteristic absorption features in the mid- and long-wave infrared region. This study considers coexisting gas components along a common line of sight rather than geometrically distinct spatial plumes. We propose a compact spectral one-dimensional convolutional neural network (1D-CNN) for pixelwise gas species identification and the extraction of binary detection regions directly from 121-band spectra. Across five independently simulated NETD conditions and cross-noise train–test evaluations, the model maintained consistently strong classification performance over the examined noise range. On the measured scenes, the proposed model was compared with four traditional detectors and an adapted spectral Transformer baseline, showing a favorable and comparatively consistent Precision–Recall balance across the four target gases. These results support a fixed-platform field proof of concept, but do not constitute airborne-platform, edge deployment, quantitative concentration retrieval, or broad cross-site validation.