An unmixing-based method for the analysis of thermal hyperspectral images

Manuel Cubero-Castan, Jocelyn Chanussot, Xavier Briottet, Michal Shimoni, Véronique Achard

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

The estimation of surface emissivity and temperature from thermal hyperspectral data is a challenge. Methods that estimate the temperature and emissivity on a pixel composed by one single material exist. However, the estimation of the temperature on a mixed pixel, i.e. a pixel composed by more than one material, is more complex and has scarcely been investigated in the literature. This paper addresses this issue by proposing an estimator which linearizes the Black Body law around the mean temperature of each material. The performance of this estimator is studied using simulated data with different hyperspectral sensor configurations and under various noise conditions. The obtained results are encouraging and show an accuracy on the estimated temperature of 0.5 K while using high spectral resolution sensor.

OriginalspracheEnglisch
Titel2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten7809-7813
Seitenumfang5
ISBN (Print)9781479928927
DOIs
PublikationsstatusVeröffentlicht - 2014
Veranstaltung2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italien
Dauer: 4 Mai 20149 Mai 2014

Publikationsreihe

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Konferenz

Konferenz2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
Land/GebietItalien
OrtFlorence
Zeitraum4/05/149/05/14

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