Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging
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...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Journal of Spectroscopy |
| Online Access: | http://dx.doi.org/10.1155/2016/5428762 |
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| author | Ramzi Idoughi Thomas H. G. Vidal Pierre-Yves Foucher Marc-André Gagnon Xavier Briottet |
| author_facet | Ramzi Idoughi Thomas H. G. Vidal Pierre-Yves Foucher Marc-André Gagnon Xavier Briottet |
| author_sort | Ramzi Idoughi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-42119abdbb3140b08627812abb4fcf47 |
| institution | Kabale University |
| issn | 2314-4920 2314-4939 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Spectroscopy |
| spelling | doaj-art-42119abdbb3140b08627812abb4fcf472025-08-20T03:55:43ZengWileyJournal of Spectroscopy2314-49202314-49392016-01-01201610.1155/2016/54287625428762Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal ImagingRamzi Idoughi0Thomas H. G. Vidal1Pierre-Yves Foucher2Marc-André Gagnon3Xavier Briottet4ONERA, The French Aerospace Lab, DOTA, 2 Avenue Edouard Belin, 31400 Toulouse, FranceONERA, The French Aerospace Lab, DOTA, 2 Avenue Edouard Belin, 31400 Toulouse, FranceONERA, The French Aerospace Lab, DOTA, 2 Avenue Edouard Belin, 31400 Toulouse, FranceTelops Inc., 100-2600 St-Jean-Baptiste Avenue, Québec, QC, G2E 6J5, CanadaONERA, The French Aerospace Lab, DOTA, 2 Avenue Edouard Belin, 31400 Toulouse, FranceHyperspectral 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.http://dx.doi.org/10.1155/2016/5428762 |
| spellingShingle | Ramzi Idoughi Thomas H. G. Vidal Pierre-Yves Foucher Marc-André Gagnon Xavier Briottet Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging Journal of Spectroscopy |
| title | Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging |
| title_full | Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging |
| title_fullStr | Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging |
| title_full_unstemmed | Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging |
| title_short | Background Radiance Estimation for Gas Plume Quantification for Airborne Hyperspectral Thermal Imaging |
| title_sort | background radiance estimation for gas plume quantification for airborne hyperspectral thermal imaging |
| url | http://dx.doi.org/10.1155/2016/5428762 |
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