Fast and accurate quantification of gas fluxes emitted by volcanoes is essential for the risk mitigation of explosive eruption, and for the fundamental understanding of shalloweruptive processes. Sulphur dioxide (SO2), in particular, is a reliable indicator to predict upcoming eruptions, and its systemic characterization allows the rapid assessment of sudden changes in eruptive dynamics. In this regard, infrared (IR) hyperspectral imaging is a promising new technology for accurately measure SO2 fluxes day and night at a frame rate down to 1 image per second. The thermal infrared region is not very sensitive to particle scattering, which is an asset for the study of volcanic plume. A ground based infrared hyperspectral imager was deployed during the IMAGETNA campaign in 2015 and provided high spectral resolution images of the Mount Etna (Sicily, Italy) plume from the North East Crater (NEC), mainly. The LongWave InfraRed (LWIR) hyperspectral imager, hereafter name Hyper-Cam, ranges between 850–1300 cm-1 (7.7–11.8 µm). The LATMOS (Laboratoire Atmosphères Milieux Observations Spatiales) Atmospheric Retrieval Algorithm (LARA), which is used to retrieve the slant column densities (SCD) of SO2, is a robust and a complete radiative transfer model, well adapted to the inversion of round-based remote measurements. However, the calculation time to process the raw data and retrieve the infrared spectra, which is about seven days for the retrieval of one image of SO2 SCD, remains too high to infer near real-time (NRT) SO2 emission fluxes. A spectral image classification methodology based on two parameters extracting spectral features in the O3 and SO2 emission bands was developed to create a library. The relevance is evaluated in detail through tests. From data acquisition to the generation of SO2 SCD images, this method requires only ~40 s per image, which opens the possibility to infer NRT estimation of SO2 emission fluxes from IR hyperspectral imager measurements.