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Temperature and emissivity separation from Thermal Airborne Hyperspectral Imager (TASI) data / Yang Hang in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 12 (December 2013) . - pp 1099 - 1107 Titre : Temperature and emissivity separation from Thermal Airborne Hyperspectral Imager (TASI) data Type de document : Article/Communication Auteurs : Yang Hang, Auteur ; Zhang Lifu, Auteur ; Gao Yingqian, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 1099 - 1107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Chine [Termes IGN] correction atmosphérique [Termes IGN] emissivité [Termes IGN] étalonnage radiométrique [Termes IGN] image hyperspectrale [Termes IGN] image TASI [Termes IGN] radiance [Termes IGN] température au sol Résumé : (Auteur) The Thermal Airborne Hyperspectral Imager (TASI) acquires 32 bands to provide continuous spectral coverage in the wavelength range of 8 to 11.5um. The instrument was used during a field campaign in the City of Shijiazhuang, Hebei Province, China, in 2010. Land surface temperature and emissivity were measured near simultaneous with the airborne campaign for calibration and validation of the instrument. Radiance calibration was performed band-by-band using calibration coefficients, and atmospheric correction was performed using data from in situ measurements and the MODTRAN model. Surface temperature and emissivity separation were determined using the ASTER temperature-emissivity separation (ASTER_TES) and iterative spectral smooth temperature and emissivity separation (ISSTES) methods. The ASTER_TES method resulted in satisfactory agreement with ground data, with root mean square error (RMSE) values of 2.2 K for temperature and 0.0460 for broad-emissivity. The ISSTES method provided better ground validation results, with a RMSE for temperature of 1.8 K and a RMSE for broad-emissivity of 0.0394. The emissivity shapes acquired by the two methods were very similar. The results have relevance to studies of global climate change, environmental monitoring, classification, feature mining, and target recognition. Numéro de notice : A2013-688 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.12.1099 En ligne : https://doi.org/10.14358/PERS.79.12.1099 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32824 [article]