光谱学与光谱分析 |
|
|
|
|
|
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 |
|
|
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.
|
Received: 2009-12-26
Accepted: 2010-03-28
|
|
Corresponding Authors:
ZHENG Qiu-gen
E-mail: 342619668@qq.com
|
|
[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.
|
[1] |
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. |
[2] |
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. |
[3] |
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. |
[4] |
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. |
[5] |
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. |
[6] |
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. |
[7] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[8] |
CUI Zhen-zhen1, 2, MA Chao1, ZHANG Hao2*, ZHANG Hong-wei3, LIANG Hu-jun3, QIU Wen2. Absolute Radiometric Calibration of Aerial Multispectral Camera Based on Multi-Scale Tarps[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3571-3581. |
[9] |
TAO Jing-zhe1, 3, SONG De-rui1, 3, SONG Chuan-ming2, WANG Xiang-hai1, 2*. Multi-Band Remote Sensing Image Sharpening: A Survey[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 2999-3008. |
[10] |
FU Xiao-man1, 2, BAO Yu-long1, 2*, Bayaer Tubuxin1, 2, JIN Eerdemutu1, 2, BAO Yu-hai1, 2. Spectral Characteristics Analysis of Desert Steppe Vegetation Based on Field Online Multi-Angle Spectrometer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3170-3179. |
[11] |
CHEN Hao1, 2, WANG Hao3*, HAN Wei3, GU Song-yan4, ZHANG Peng4, KANG Zhi-ming1. Impact Analysis of Microwave Real Spectral Response on Rapid Radiance Simulation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3260-3265. |
[12] |
WANG Lin, WANG Xiang*, ZHOU Chao, WANG Xin-xin, MENG Qing-hui, CHEN Yan-long. Remote Sensing Quantitative Retrieval of Chlorophyll a and Trophic Level Index in Main Seagoing Rivers of Lianyungang[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3314-3320. |
[13] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
[14] |
JIN Chun-bai1, YANG Guang1*, LU Shan2*, LIU Wen-jing1, LI De-jun1, ZHENG Nan1. Band Selection Method Based on Target Saliency Analysis in Spatial Domain[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2952-2959. |
[15] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
|
|
|
|