Scene-based spectral calibration for thermal infrared hyperspectral data

Xie Feng; Liu Chengyu; Shao Honglan; Zhang Changxing; Yang Gui; Wang Jianyu

doi:10.3788/IRLA201746.0138001

Volume 46 Issue 1

Feb. 2017

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Article Navigation > Infrared and Laser Engineering > 2017 > 46(1): 138001-0138001(6)

Xie Feng, Liu Chengyu, Shao Honglan, Zhang Changxing, Yang Gui, Wang Jianyu. Scene-based spectral calibration for thermal infrared hyperspectral data[J]. Infrared and Laser Engineering, 2017, 46(1): 138001-0138001(6). doi: 10.3788/IRLA201746.0138001

Citation:

Xie Feng, Liu Chengyu, Shao Honglan, Zhang Changxing, Yang Gui, Wang Jianyu. Scene-based spectral calibration for thermal infrared hyperspectral data[J]. Infrared and Laser Engineering, 2017, 46(1): 138001-0138001(6). doi: 10.3788/IRLA201746.0138001

Scene-based spectral calibration for thermal infrared hyperspectral data

doi: 10.3788/IRLA201746.0138001

1.
Key Lab of Spatial Active Opto-Electronic Techniques,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China

Received Date: 2016-08-10
Rev Recd Date: 2016-09-20
Publish Date: 2017-01-25

Abstract

Compared with a given laboratory calibration, systematic shifts on the center wavelength and the Full Width at Half Maximum (FWHM) of each band of a hyperspectral sensor, will emerge as the imagery environment changes. The center wavelength shift and the FWHM variation have influence on the inversion precision of the emissivity and temperature, especially near the atmospheric absorption bands. A technical process of spectral calibration for thermal infrared hyperspectral data was proposed, which was verified through a simulation experiment with the water vapor absorption band at 11.73 m selected as the reference band. The experiment shows when the spectral resolution is 50 nm, the center wavelength shift ranges from -50 nm to 50 nm and the FWHM variation ranges from -25 nm to 25 nm, atmospheric water vapor content has the most influence on the error of the estimated wavelength shift and the FWHM variation. Meanwhile, the error distribution were also fitted using different surface functions, and the error distribution models used to estimate the errors were obtained. When the atmospheric water vapor content was high enough, the estimation error of the spectral center wavelength shift was able to reach within 1 nm. Finally, the proposed approach was applied to spectral calibration of the airborne thermal infrared hyperspectral data obtained by a push-broom hyperspectral thermal-infrared imager. The results show that the center wavelength shift of the thermal infrared hyperspectral imager is 28.4 nm and the FWHM variation of the imager is -18.5 nm.
- thermal infrared hyperspectral data,
- spectral calibration,
- center wavelength shift,
- FWHM variation

References

[1]	Goetz A, Heidebrecht K, Chrien T. High accuracy in-flight wavelength calibration of imaging spectrometry data[C]//Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, 1995.
[2]	Kaiser R D. Wavelength calibration and instrument line shape estimation of a LWIR hyperspectral sensor from in-scene data[C]//AeroSense 2000, International Society for Optics and Photonics, 2000:284-287.
[3]	Barry P S, Shepanski J, Segal C. On-orbit spectral calibration verification of Hyperion[C]//Geoscience and Remote Sensing Symposium, 2001, 6:2535-2537.
[4]	Gao B C, Montes M J, Davis C O. A curve-fitting technique to improve wavelength calibrations of imaging spectrometer data[C]//JPL Airborne Earth Sci, 2002:99-105.
[5]	Felde G W, Anderson G P, Cooley T W, et al. Analysis of Hyperion data with the FLAASH atmospheric correction algorithm[C]//Geoscience and Remote Sensing Symposium,2003, 1:90-92.
[6]	Gao B C, Montes M J, Davis C O. Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique[J]. Remote Sensing of Environment, 2004, 90(4):424-433.
[7]	Delwart S, Preusker R, Bourg L, et al. MERIS in-flight spectral calibration[J]. International Journal of Remote Sensing, 2007, 28(3-4):479-496.
[8]	Dadon A, Ben-dor E, Karnieli A. Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (smile) in Hyperion images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6):2603-2612.
[9]	Green R O, Pavri B E, Chrien T G. On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(6):1194-1203.
[10]	Wang Tianxing, Yan Guang, Ren Huazhong, et al. Improved methods for spectral calibration of on-orbit imaging spectrometers[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2010, 48(11):3924-3931.
[11]	Yan Lei, Gou Zhiyang, Zhao Hongying, et al. In-flight spectral calibration of UAV hyperspectral imager based on Radiance Matching[J]. Journal of Infrared Millim.Waves, 2013, 31(6):517-522. (in Chinese)晏磊, 勾志阳, 赵红颖, 等. 基于辐亮度匹配的无人机载成像光谱仪外场光谱定标研究[J]. 红外与毫米波学报, 2013, 31(6):517-522.
[12]	Liu Yaokai, Wang Tianxing, Ma Lingling, et al. Spectral calibration of hyperspectral data observed from a hyperspectrometer loaded on an unmanned aerial vehicle platform[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2630-2638.
[13]	Zhang Chunlei, Xiang Yang. Spectral calibration of the hyperspectral imager based on atmosphere absorption[J]. Spectroscopy and Spectral Analysis, 2012, 32(1):268-272. (in Chinese)张春雷, 向阳. 基于大气吸收带的超光谱成像仪光谱定标技术研究[J]. 光谱学与光谱分析, 2012, 32(1):268-272.
[14]	Chen Hongyao, Zhang Liming, Li Xin, et al. Hyperspectral sensor in flight spectral calibration based on characteristic spectra of atmosphere[J]. Acta Opatica Sinica, 2013, 33(5):287-293. (in Chinese)陈洪耀, 张黎明, 李鑫, 等. 高光谱遥感器飞行中基于大气特征谱线的光谱定标技术[J]. 光学学报, 2013, 33(5):287-293.
[15]	Gao Hailiang, Gu Xingfa, Yu Tao, et al. In-flight spectral calibration of oxygen absorption channels of hyperspectral sensor[J]. Acta Photonica Sinica, 2014, 43(10):58-65. (in Chinese)高海亮, 顾行发, 余涛, 等. 氧气吸收通道的高光谱传感器在轨光谱定标[J]. 光子学报, 2014, 43(10):58-65.
[16]	Brazile J, Neville R A, Staenz K, et al. Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features[J]. Canadian Journal of Remote Sensing, 2008, 34(S1):43-58.
[17]	Guanter L, Richter R, Moreno J. Spectral calibration of hyperspectral imagery using atmospheric absorption features[J]. Applied Optics, 2006, 45(10):2360-2370.
[18]	Guanter L, Segl K, Sang B, et al. Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers[J]. Optics Express, 2009, 17(14):11594-11606.
[19]	Seiler M C, Seiler F A. Numerical recipes in C:the art of scientific computing[J]. Risk Analysis, 1989, 9(3):415-416.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Scene-based spectral calibration for thermal infrared hyperspectral data

1. Key Lab of Spatial Active Opto-Electronic Techniques,Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China

Received Date: 2016-08-10

Rev Recd Date: 2016-09-20

Publish Date: 2017-01-25

Key words:

Abstract: Compared with a given laboratory calibration, systematic shifts on the center wavelength and the Full Width at Half Maximum (FWHM) of each band of a hyperspectral sensor, will emerge as the imagery environment changes. The center wavelength shift and the FWHM variation have influence on the inversion precision of the emissivity and temperature, especially near the atmospheric absorption bands. A technical process of spectral calibration for thermal infrared hyperspectral data was proposed, which was verified through a simulation experiment with the water vapor absorption band at 11.73 m selected as the reference band. The experiment shows when the spectral resolution is 50 nm, the center wavelength shift ranges from -50 nm to 50 nm and the FWHM variation ranges from -25 nm to 25 nm, atmospheric water vapor content has the most influence on the error of the estimated wavelength shift and the FWHM variation. Meanwhile, the error distribution were also fitted using different surface functions, and the error distribution models used to estimate the errors were obtained. When the atmospheric water vapor content was high enough, the estimation error of the spectral center wavelength shift was able to reach within 1 nm. Finally, the proposed approach was applied to spectral calibration of the airborne thermal infrared hyperspectral data obtained by a push-broom hyperspectral thermal-infrared imager. The results show that the center wavelength shift of the thermal infrared hyperspectral imager is 28.4 nm and the FWHM variation of the imager is -18.5 nm.

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Reference (19)

[1]	Goetz A, Heidebrecht K, Chrien T. High accuracy in-flight wavelength calibration of imaging spectrometry data[C]//Summaries of the Fifth Annual JPL Airborne Earth Science Workshop, 1995.
[2]	Kaiser R D. Wavelength calibration and instrument line shape estimation of a LWIR hyperspectral sensor from in-scene data[C]//AeroSense 2000, International Society for Optics and Photonics, 2000:284-287.
[3]	Barry P S, Shepanski J, Segal C. On-orbit spectral calibration verification of Hyperion[C]//Geoscience and Remote Sensing Symposium, 2001, 6:2535-2537.
[4]	Gao B C, Montes M J, Davis C O. A curve-fitting technique to improve wavelength calibrations of imaging spectrometer data[C]//JPL Airborne Earth Sci, 2002:99-105.
[5]	Felde G W, Anderson G P, Cooley T W, et al. Analysis of Hyperion data with the FLAASH atmospheric correction algorithm[C]//Geoscience and Remote Sensing Symposium,2003, 1:90-92.
[6]	Gao B C, Montes M J, Davis C O. Refinement of wavelength calibrations of hyperspectral imaging data using a spectrum-matching technique[J]. Remote Sensing of Environment, 2004, 90(4):424-433.
[7]	Delwart S, Preusker R, Bourg L, et al. MERIS in-flight spectral calibration[J]. International Journal of Remote Sensing, 2007, 28(3-4):479-496.
[8]	Dadon A, Ben-dor E, Karnieli A. Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (smile) in Hyperion images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6):2603-2612.
[9]	Green R O, Pavri B E, Chrien T G. On-orbit radiometric and spectral calibration characteristics of EO-1 Hyperion derived with an underflight of AVIRIS and in situ measurements at Salar de Arizaro, Argentina[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2003, 41(6):1194-1203.
[10]	Wang Tianxing, Yan Guang, Ren Huazhong, et al. Improved methods for spectral calibration of on-orbit imaging spectrometers[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2010, 48(11):3924-3931.
[11]	Yan Lei, Gou Zhiyang, Zhao Hongying, et al. In-flight spectral calibration of UAV hyperspectral imager based on Radiance Matching[J]. Journal of Infrared Millim.Waves, 2013, 31(6):517-522. (in Chinese)晏磊, 勾志阳, 赵红颖, 等. 基于辐亮度匹配的无人机载成像光谱仪外场光谱定标研究[J]. 红外与毫米波学报, 2013, 31(6):517-522.
[12]	Liu Yaokai, Wang Tianxing, Ma Lingling, et al. Spectral calibration of hyperspectral data observed from a hyperspectrometer loaded on an unmanned aerial vehicle platform[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6):2630-2638.
[13]	Zhang Chunlei, Xiang Yang. Spectral calibration of the hyperspectral imager based on atmosphere absorption[J]. Spectroscopy and Spectral Analysis, 2012, 32(1):268-272. (in Chinese)张春雷, 向阳. 基于大气吸收带的超光谱成像仪光谱定标技术研究[J]. 光谱学与光谱分析, 2012, 32(1):268-272.
[14]	Chen Hongyao, Zhang Liming, Li Xin, et al. Hyperspectral sensor in flight spectral calibration based on characteristic spectra of atmosphere[J]. Acta Opatica Sinica, 2013, 33(5):287-293. (in Chinese)陈洪耀, 张黎明, 李鑫, 等. 高光谱遥感器飞行中基于大气特征谱线的光谱定标技术[J]. 光学学报, 2013, 33(5):287-293.
[15]	Gao Hailiang, Gu Xingfa, Yu Tao, et al. In-flight spectral calibration of oxygen absorption channels of hyperspectral sensor[J]. Acta Photonica Sinica, 2014, 43(10):58-65. (in Chinese)高海亮, 顾行发, 余涛, 等. 氧气吸收通道的高光谱传感器在轨光谱定标[J]. 光子学报, 2014, 43(10):58-65.
[16]	Brazile J, Neville R A, Staenz K, et al. Toward scene-based retrieval of spectral response functions for hyperspectral imagers using Fraunhofer features[J]. Canadian Journal of Remote Sensing, 2008, 34(S1):43-58.
[17]	Guanter L, Richter R, Moreno J. Spectral calibration of hyperspectral imagery using atmospheric absorption features[J]. Applied Optics, 2006, 45(10):2360-2370.
[18]	Guanter L, Segl K, Sang B, et al. Scene-based spectral calibration assessment of high spectral resolution imaging spectrometers[J]. Optics Express, 2009, 17(14):11594-11606.
[19]	Seiler M C, Seiler F A. Numerical recipes in C:the art of scientific computing[J]. Risk Analysis, 1989, 9(3):415-416.

Scene-based spectral calibration for thermal infrared hyperspectral data

doi: 10.3788/IRLA201746.0138001

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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