光谱学与光谱分析 |
|
|
|
|
|
Application of Single-Band Brightness Variance Ratio to the Interference Dissociation of Cloud for Satellite Data |
QU Wei-ping1, 2, LIU Wen-qing1, LIU Jian-guo1, LU Yi-huai1, ZHU Jun1, QIN Min1, LIU Cheng3 |
1. Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China 2. Department of the Computer Science and Technology, Anhui University of Science and Technology, Huainan 232001, China 3. Center for Environmental Remote Sensing, Chiba University, Chiba, Japan |
|
|
Abstract In satellite remote-sensing detection, cloud as an interference plays a negative role in data retrieval. How to discern the cloud fields with high fidelity thus comes as a need to the following research. A new method rooting in atmospheric radiation characteristics of cloud layer, in the present paper, presents a sort of solution where single-band brightness variance ratio is used to detect the relative intensity of cloud clutter so as to delineate cloud field rapidly and exactly, and the formulae of brightness variance ratio of satellite image, image reflectance variance ratio, and brightness temperature variance ratio of thermal infrared image are also given to enable cloud elimination to produce data free from cloud interference. According to the variance of the penetrating capability for different spectra bands, an objective evaluation is done on cloud penetration of them with the factors that influence penetration effect. Finally, a multi-band data fusion task is completed using the image data of infrared penetration from cirrus nothus. Image data reconstruction is of good quality and exactitude to show the real data of visible band covered by cloud fields. Statistics indicates the consistency of waveband relativity with image data after the data fusion.
|
Received: 2005-11-08
Accepted: 2006-02-08
|
|
Corresponding Authors:
QU Wei-ping
|
|
Cite this article: |
QU Wei-ping,LIU Wen-qing,LIU Jian-guo, et al. Application of Single-Band Brightness Variance Ratio to the Interference Dissociation of Cloud for Satellite Data [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2006, 26(11): 2011-2015.
|
|
|
|
URL: |
https://www.gpxygpfx.com/EN/Y2006/V26/I11/2011 |
[1] 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(1):47. [2] Bréon F M. Remote Sensing of Environment, 1993, 43(2): 179. [3] Wang Xuanji, Key Jeffrey R. SPIE, 2002, 4815: 97. [4] Chen Yan, Sun-Mack Sunny. SPIE, 2003, 4891: 361. [5] LIU Yu-jie, YANG Zhong-dong(刘玉洁,杨忠东). Theory and Algorithm for MODIS Remote Sensing Data Processing(MODIS遥感信息处理原理与算法). Beijing: Science Press(北京: 科学出版社),2001. 115. [6] Jones Brenda, Tolk Brian. SPIE, 2002, 4814: 402. [7] Burke Hsiao-hua, Griffin Michael K. SPIE, 2000, 4049: 433. [8] Gao Bo-Cai, Davis Cuttiss O, Kaufman Yoram J. SPIE, 1997, 3122: 78. [9] Gallaudet T G, Simpson J J. Remote Sensing of Environment, 1991, 38(2): 77. [10] CHEN Wei-min(陈渭民). Satellite Meteorology(卫星气象学). Beijing: Meteorology Publishing House(北京: 气象出版社), 2003. 123. [11] Qu Weiping, Liu Wenqing, Liu Jianguo. SPIE, 2005, 5832: 392. [12] XU Guang-tong, YUAN Hong-fu, LU Wan-zhen(徐广通, 袁洪福, 陆婉珍). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2000, 20(5): 619.
|
[1] |
WU Chao1, QIU Bo1*, PAN Zhi-ren1, LI Xiao-tong1, WANG Lin-qian1, CAO Guan-long1, KONG Xiao2. Application of Spectral and Metering Data Fusion Algorithm in Variable Star Classification[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1869-1874. |
[2] |
WANG Wen-jun1, SHA Yun-fei1, WANG Yang-zhong1, YU Jie1, LIU Tai-ang2, ZHANG Xu-feng3, MENG Xiang-zhou3, GE Jiong1*. Discriminating Flavor Styles via Data Fusion of NIR and EN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 133-137. |
[3] |
CHEN Feng-xia1, YANG Tian-wei2, LI Jie-qing1, LIU Hong-gao3, FAN Mao-pan1*, WANG Yuan-zhong4*. Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 549-554. |
[4] |
WANG Dong1,2, HAN Ping1,2*, WU Jing-zhu3*, ZHAO Li-li4, XU Heng4. Non-Destructive Identification of the Heat-Damaged Kernels of Waxy Corn Seeds Based on Near-Ultraviolet-Visible-Shortwave and Near-Infrared Multi-Spectral Imaging Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2696-2702. |
[5] |
ZHANG Jiao1, 2, WANG Yuan-zhong1, YANG Wei-ze1, ZHANG Jin-yu1*. Data Fusion of ATR-FTIR and UV-Vis Spectra to Identify the Origin of Polygonatum Kingianum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1410-1416. |
[6] |
SHA Yun-fei1, HUANG Wen1, WANG Liang1, LIU Tai-ang2,YUE Bao-hua2, LI Min-jie2, YOU Jing-lin2, GE Jiong1*, XIE Wen-yan1*. Merging MIR and NIR Spectral Data for Flavor Style Determination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 473-477. |
[7] |
CHEN Ying1,XU Yang-mei1, DI Yuan-jian1,CUI Xing-ning1,ZHANG Jie1,ZHOU Xin-de1,XIAO Chun-yan2, LI Shao-hua3. COD Concentration Prediction Model Based on Multi-Spectral Data Fusion and GANs Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(01): 188-193. |
[8] |
YU Ye-xia1,2, LI Li1*, WANG Yuan-zhong2*. Study on Differentiation of Swertia leducii and Its Closely Relative Species Based on Data Fusion of Spectra and Chromatography[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(08): 2440-2446. |
[9] |
HU Yi-ran1, LI Jie-qing1, LIU Hong-gao2, FAN Mao-pan1*, WANG Yuan-zhong3*. Infrared Spectral Study on the Origin Identification of Boletus Tomentipes Based on the Random Forest Algorithm and Data Fusion Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(05): 1495-1502. |
[10] |
HU Yi-ran1, LI Jie-qing1, LIU Hong-gao2, FAN Mao-pan1*, WANG Yuan-zhong3*. The Origin Identification Study of Boletus Edulis Based on the Infrared Spctrum Data Fusion Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1276-1282. |
[11] |
CHEN Ying1, HE Lei1, CUI Xing-ning1, HAN Shuai-tao1, ZHU Qi-guang2, ZHAI Ying-jian3, LI Shao-hua3. Study on Mixed Prediction Model of Nitrate Concentration in Water Based on Ultraviolet Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(05): 1489-1494. |
[12] |
ZHANG Yu1, 2, LI Jie-qing1, LI Tao3, LIU Hong-gao1*, WANG Yuan-zhong2*. Application of 17 Classification Algorithms for Authentication Research of Various Boletus[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(02): 448-453. |
[13] |
ZHANG Yu1,2, LI Jie-qing1, LI Tao3, LIU Hong-gao1*, WANG Yuan-zhong2*. Discrimination of Geographical Origins of Boletus Edulis Using Data Fusion Combined Mineral Elements with FTIR Spectrum of Different Parts[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(10): 3070-3076. |
[14] |
ZHANG Yu1, 2, LI Jie-qing1, LI Tao3, LIU Hong-gao1*, WANG Yuan-zhong2*. Study on the Geographical Traceability of Boletus Tomentipes Using Multi-Spectra Data Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2529-2535. |
[15] |
HU Jing, TANG Guo, DENG Hai-yan, XIONG Yan-mei*. Determination of Active Ingredient in Emamectin Benzoate Formulation by Data Fusion Strategy Based on Near/Mid Infrared Spectra and Competitive Adaptive Reweighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(04): 1297-1301. |
|
|
|
|