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Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, Faculty of Science & Engineering. FOI, SE-58111 Linkoping, Sweden.ORCID iD: 0000-0002-6763-5487
2017 (English)In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 55, no 2, 658-670 p.Article in journal (Refereed) Published
Abstract [en]

We address the problem of estimating atmosphere parameters (temperature and water vapor content) from data captured by an airborne thermal hyperspectral imager and propose a method based on linear and nonlinear optimization. The method is used for the estimation of the parameters (temperature and emissivity) of the observed object as well as sensor gain under certain restrictions. The method is analyzed with respect to sensitivity to noise and the number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that the estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. The proposed method is also extended to exploit additional knowledge, for example, measurements of atmospheric parameters and sensor noise. Additionally, we show how to extend the method in order to improve spectral calibration.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2017. Vol. 55, no 2, 658-670 p.
Keyword [en]
Infrared imaging; infrared spectroscopy; optimization methods; remote sensing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-134597DOI: 10.1109/TGRS.2016.2613685ISI: 000392391800004OAI: oai:DiVA.org:liu-134597DiVA: diva2:1076021
Note

Funding Agencies|Swedish Research Council under Project EMC2

Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-03-16

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