光谱学与光谱分析 |
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Application of Dark Pixels Atmospheric Correction Algorithm to Hyperion Imageries |
ZHENG Qiu-gen1,QUAN Wen-ting3 |
1. College of Ocean Sciences, China University of Geosciences, Beijing 100083, China 2. College of Resources Sciences and Technology, Beijing Normal University, Beijing 100875, China |
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Abstract Atmosphere is an important factor that affects the quantitative analysis and application of remote sensing technology. In the support of IDL platform, the study takes advantages of dark pixel atmospheric correction algorithm (DPACA) to extract the optical depth of atmosphere, and then remove the atmospheric influences from each channel of the Hyperion sensor by that atmospheric parameter. The study results show that the optical depth decreases with the increase in central wavelength of the Hyperion sensor, namely the optical depth is negative versus the central wavelength of sensor. The linear model is the optimal experimental model that is used to describe that relationship, and its correlative coefficient is 0.912 3. It was found that the signals recorded by the remote sensing sensor can’t express the inherent optical properties and apparent optical properties in a proper manner. Additionally, the remote sensing signals are insensitive to the variations of waters qualities’ samples. At the blue and green bands, the effects of atmosphere are the most serious. The spectra are completely different from the optical properties of natural waters at those bands. Compared with the theoretical spectral features of waters’ optical properties, the image quality of Hyperion sensor has been perfectly improved by the DPACA. Under the condition of lacking the vertical profile data of atmosphere, the DPACA is an available approach to removing the atmospheric influence on the Hyperion imageries.
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Received: 2009-12-26
Accepted: 2010-03-28
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Corresponding Authors:
ZHENG Qiu-gen
E-mail: 342619668@qq.com
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[1] WU Jun-zhao, TIAN Qing-jiu, JIN Zhen-yu, et al(吴昀昭, 田庆久, 金震宇, 等). Remote Sensing Information(遥感信息),2004, (2): 9. [2] Zhao W J, Tamura M, Takahashi H. Remote Sensing of Environment,2000, 76: 202. [3] Hu C M, Muller-karger F E, Andrefouet S, et al. Remote Sensing of Environment,2001, 78: 99. [4] Siegel D A, Wang M H, Maritorena S, et al. Applied Optics,2000, 39(21): 3582. [5] ZHENG Wei, ZENG Zhi-yuan(郑 伟, 曾志远). Remote Sensing for Land & Resources(国土资源遥感),2005, (1): 8. [6] TANG Bing-xiang, LI Zeng-yuan, CHEN Er-xue, et al(谭炳香, 李增元, 陈尔学, 等). Remote Sensing Information(遥感信息),2005, (6): 36. [7] WU Jian, HE Ting, CHENG Peng-gen(吴 剑, 何 挺, 程朋根). Progress in Geography (地理科学进展),2006, 25(2): 131. [8] LIU Xiao-ping, DENG Ru-ru, PENG Xiao-juan(刘小平, 邓孺孺, 彭晓鹃). Scientia Geographica Sinica(地理科学),2005, 25(1): 87. [9] CHEN Lei, DENG Ru-ru, KE Rui-peng, et al(陈 蕾, 邓孺孺, 柯锐鹏, 等). Geography and Geo-Information Science(地理与地理信息科学),2004, 20(2): 34. [10] Gordon H R, Clark D K. Applied Optics,1981, 20(24): 4175. [11] Ding K Y, Gordon H R. Applied Optics,1994, 33(30): 7096. [12] Gordon H R, Castano D J. Applied Optics,1987,26(11): 2111. [13] ZHANG Ting-lu, SHI Ying-ni(张亭禄, 施英妮). Periodical of Ocean University of China(中国海洋大学学报), 2005, 35(5): 849. [14] Dekker A G,Vos R J,Peters S W M. International Journal of Remote Sensing,2002, 23(1): 15. [15] Gower J F R,Doerffer R,Borstad G A. International Journal of Remote Sensing,1999, 20(9): 1771. [16] Doxaran D, Froidefond J M, Lavender S, et al. Remote Sensing of Environment,2002, 81:149.
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