&lt;title&gt;New approach to atmospheric correction of hyperspectral data&lt;/title&gt;

Abel, Michael D.; Zenner, Jill M.; Petrick, Gary A.; Buswell, Alan T.; Pilati, Martin L.; Czyzewski, William R.; Alessandro, Lawrence P.; Weaver, Sandra K.

doi:10.1117/12.478738

Cited by 2 publications

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“…Also, the methods in the literature require either highresolution spectral data, or the use of spectral features in other wavelengths than the ones we study here. 2,3 The most well-known methods for atmospheric correction are probably AAC 4 and ISAC. 5 Extensions include ARTEMISS, 6 where a database of atmospheric transmissions is used and a smooth emissivity spectrum is retrieved.…”

Section: Related Workmentioning

confidence: 99%

Estimating atmosphere parameters in hyperspectral data

Ahlberg

2010

SPIE Proceedings

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We address the problem of estimating atmosphere parameters (temperature, water vapour content) from data captured by an airborne thermal hyperspectral imager, and propose a method based on direct optimization. The method also involves the estimation of object parameters (temperature and emissivity) under the restriction that the emissivity is constant for all wavelengths. Certain sensor parameters can be estimated as well in the same process. The method is analyzed with respect to sensitivity to noise and number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. More than 20 bands does not improve the estimates. The proposed method is also extended to incorporate additional knowledge, for example measurements of atmospheric parameters and sensor noise.

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Section: Related Workmentioning

confidence: 99%

Estimating atmosphere parameters in hyperspectral data

Ahlberg

2010

SPIE Proceedings

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“…where T w is given by (13) and L o t,ε is given by (3). τ d , τ v , and L ↓ are computed using MODTRAN .…”

Section: F Summarizing the Modelsmentioning

confidence: 99%

Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery

Ahlberg

2017

IEEE Trans. Geosci. Remote Sensing

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We address the problem of estimating atmosphere parameters (temperature, water vapor content) from data captured by an airborne thermal hyperspectral imager, and propose a method based on linear and non-linear optimization.The method is used for estimation of the parameters (temperature and emissivity) of the observed object, as well as for sensor gain under certain restrictions. The method is analyzed with respect to sensitivity to noise and number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that 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.

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<title>New approach to atmospheric correction of hyperspectral data</title>

Cited by 2 publications

References 1 publication

Estimating atmosphere parameters in hyperspectral data

Estimating atmosphere parameters in hyperspectral data

Optimizing Object, Atmosphere, and Sensor Parameters in Thermal Hyperspectral Imagery

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