Spectra classification methodology for hyperspectral InfraRed imaging of
Mt Etna volcanic plume with a radiative transfer retrieval model
Abstract
Quantification of sulfur dioxide (SO2) emission flux is a fundamental
task in volcanology to have insights of the composition and the spatial
evolution of volcanic plumes. The ground based InfraRed hyperspectral
imager HyperCam, from Telops Company, was deployed during IMAGETNA
campaign in 2015 and provided high spatial and spectral resolution
images of Mt Etna plume. The spectral range of the hyperspectral imager
is [7.7 - 11.8 µm] and the measured images contained 320 x 64 pixels
with a spectral resolution of 2 cm-1. To process hyperspectral images in
quasi real-time, a fast and reliable radiative transfer retrieval model
is required. The LATMOS Atmospheric Retrieval Algorithm (LARA), used to
retrieve the slant column densities of SO2, includes an accurate
line-by-line radiative transfer model and an efficient minimization
algorithm of the Levenberg-Marquardt type. But the calculation time
remains too high to infer near real time (NRT) estimation of SO2 fluxes.
As first, to reach NRT target, a classification methodology of the
brightness temperature spectra was developed and then applied on each
measured sequence to significantly decrease the processing time. One
image previously took a week of calculation to be retrieved. The
classification of the spectra, which takes a couple of hours, allows the
retrieval of a complete measurement sequence of the field campaign
(~400 images) in only two days. The accuracy of the
methodology was confirmed, by comparing the SO2 slant column density
images obtained after classification with the one obtained by the
accurate and time expensive pixel by pixel retrieval processing. The
LARA model, the spectra classification methodology and a comparison of
the results will be presented.