光谱学与光谱分析 |
|
|
|
|
|
The Correlation Based Mid-Infrared Temperature and Emissivity Separation Algorithm |
CHENG Jie1,3,NIE Ai-xiu2,DU Yong-ming1 |
1. Beijing Normal University, State Key Laboratory of Remote Sensing Science, Beijing 100875, China 2. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241000, China 3. Research Center for Remote Sensing and GIS, Beijing Normal University, Beijing 100875, China |
|
|
Abstract Temperature and emissivity separation is the key problem in infrared remote sensing. Based on the analysis of the relationship between the atmospheric downward radiance and surface emissivity containing atmosphere residue without the effects of sun irradiation, the present paper puts forward a temperature and emissivity separation algorithm for the ground-based mid-infrared hyperspectral data. The algorithm uses the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue as a criterion to optimize the surface temperature, and the correlation between the atmospheric downward radiance and surface emissivity containing atmosphere residue depends on the bias between the estimated surface temperature and true surface temperature. The larger the temperature bias, the greater the correlation. Once we have obtained the surface temperature, the surface emissivity can be calculated easily. The accuracy of the algorithm was evaluated with the simulated mid-infrared hyperspectral data. The results of simulated calculation show that the algorithm can achieve higher accuracy of temperature and emissivity inversion, and also has broad applicability. Meanwhile, the algorithm is insensitive to the instrumental random noise and the change in atmospheric downward radiance during the field measurements.
|
Received: 2007-11-12
Accepted: 2008-02-20
|
|
Corresponding Authors:
CHENG Jie
E-mail: brucechan2003@126.com
|
|
[1] Salisbury J W, D’Aria D M. Remote Sens. Environ., 1994, 47: 345. [2] Mushkin A, Balick L K, Gillespie A R. Remote Sens. Environ., 2005, 98: 141. [3] GAO Min-guang, LIU Wen-qing, ZHANG Tian-shu, et al(高闽光,刘文清,张天舒,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006, 26(12): 2203. [4] WEI Xiu-li, LU Yi-huai, GAO Min-guang, et al(魏秀丽,陆亦怀,高闽光,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007, 27(3): 452. [5] XU Liang, LIU Jian-guo, GAO Min-guang, et al(徐 亮,刘建国,高闽光,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2007, 27(5): 889. [6] Korb A R, Dybwad P, Wadsworth W, et al. Applied Optics, 1996, 35(10): 1679. [7] Watson K. Remote Sens. Environ., 1992, 42: 113. [8] Barducci A,Pippi I. IEEE Trans. Geosci. Remote Sens., 1996, 34(3): 681. [9] Gillespie A R, Matsunaga T, Rokugawa S, et al. IEEE Trans. Geosci. Remote Sens., 1998, 36(4): 1113. [10] Borel C C. Proceedings of IGARSS98, 1998, 1: 546. [11] Becker F, Li Z-L. Remote Sens. Environ., 1990, 32: 17. [12] CHENG Jie, XIAO Qing, LI Xiao-wen, et al(程 洁,肖 青,李小文,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2008,28(4):780. [13] Wan Z, Ng D, Dozier J. Remote Sensing of Earths Surface and Atmosphere, 1993, 14(3): 91. [14] CHENG Jie, LIU Qin-huo, LI Xiao-wen, et al(程 洁,柳钦火,李小文,等). J. Infrared Millim. Waves(红外与毫米波学报),2008,27(1):130. [15] QI Shu-hua, LUO Cheng-feng, WANG Chang-yao, et al(齐述华,骆成凤,王长耀,等). Remote Sensing Technology and Application(遥感技术与应用), 2006, 21(2): 130.
|
[1] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
[2] |
LIANG Ye-heng1, DENG Ru-ru1, 2*, LIANG Yu-jie1, LIU Yong-ming3, WU Yi4, YUAN Yu-heng5, AI Xian-jun6. Spectral Characteristics of Sediment Reflectance Under the Background of Heavy Metal Polluted Water and Analysis of Its Contribution to
Water-Leaving Reflectance[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 111-117. |
[3] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
ZHU Wen-jing1, 2,FENG Zhan-kang1, 2,DAI Shi-yuan1, 2,ZHANG Ping-ping3,JI Wen4,WANG Ai-chen1, 2,WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
[5] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[6] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[7] |
LIANG Shou-zhen1, SUI Xue-yan1, WANG Meng1, WANG Fei1, HAN Dong-rui1, WANG Guo-liang1, LI Hong-zhong2, MA Wan-dong3. The Influence of Anthocyanin on Plant Optical Properties and Remote Sensing Estimation at the Scale of Leaf[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 275-282. |
[8] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[9] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[10] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[11] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[12] |
DUAN Ming-xuan1, LI Shi-chun1, 2*, LIU Jia-hui1, WANG Yi1, XIN Wen-hui1, 2, HUA Deng-xin1, 2*, GAO Fei1, 2. Detection of Benzene Concentration by Mid-Infrared Differential
Absorption Lidar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3351-3359. |
[13] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[14] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[15] |
LI Si-yuan, JIAO Jian-nan, WANG Chi*. Specular Reflection Removal Method Based on Polarization Spectrum
Fusion and Its Application in Vegetation Health Monitoring[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3607-3614. |
|
|
|
|