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Journal of Spectroscopy
Volume 2016, Article ID 5428762, 17 pages
http://dx.doi.org/10.1155/2016/5428762
Research Article

Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging

1ONERA, The French Aerospace Lab, DOTA, 2 Avenue Edouard Belin, 31400 Toulouse, France
2Institut Supérieur de l’Aéronautique et de l’Espace (ISAE), Toulouse, France
3Telops Inc., 100-2600 St-Jean-Baptiste Avenue, Québec, QC, Canada G2E 6J5

Received 2 October 2015; Accepted 23 February 2016

Academic Editor: Hassen Aroui

Copyright © 2016 Ramzi Idoughi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Hyperspectral imaging in the long-wave infrared (LWIR) is a mean that is proving its worth in the characterization of gaseous effluent. Indeed the spectral and spatial resolution of acquisition instruments is steadily decreasing, making the gases characterization increasingly easy in the LWIR domain. The majority of literature algorithms exploit the plume contribution to the radiance corresponding to the difference of radiance between the plume-present and plume-absent pixels. Nevertheless, the off-plume radiance is unobservable using a single image. In this paper, we propose a new method to retrieve trace gas concentration from airborne infrared hyperspectral data. More particularly the outlined method improves the existing background radiance estimation approach to deal with heterogeneous scenes corresponding to industrial scenes. It consists in performing a classification of the scene and then applying a principal components analysis based method to estimate the background radiance on each cluster stemming from the classification. In order to determine the contribution of the classification to the background radiance estimation, we compared the two approaches on synthetic data and Telops Fourier Transform Spectrometer (FTS) Imaging Hyper-Cam LW airborne acquisition above ethylene release. We finally show ethylene retrieved concentration map and estimate flow rate of the ethylene release.