FY-3C/VIRR中红外图像太阳光污染订正
Study on the correction of sunlight pollution in mid-infrared image of FY-3C/VIRR
- 2021年25卷第3期 页码:803-816
纸质出版日期: 2021-03-07
DOI: 10.11834/jrs.20209474
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纸质出版日期: 2021-03-07 ,
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朱吉彪,胡秀清,杨磊库,徐寒列,徐娜,张鹏.2021.FY-3C/VIRR中红外图像太阳光污染订正.遥感学报,25(3): 803-815
Zhu J B,Hu X Q,Yang L K,Xu H L,Xu N and Zhang P. 2021. Study on the correction of sunlight pollution in mid-infrared image of FY-3C/VIRR. National Remote Sensing Bulletin, 25(3):803-815
风云三号C星(FY-3C)上搭载的可见光红外扫描辐射计VIRR (Visible and Infra-Red Radiometer)是继承风云一号卫星光学成像仪器,它在高纬度昼夜交替区域附近由于地平线附近太阳光照射,导致仪器扫描镜和其他部件产生杂散光,导致对地观测图像被污染,尤其VIRR中红外第3通道(3.7 μm)图像产生了严重条带噪声,影响了后续产品的质量精度和数据应用。本文根据图像条带噪声各向异性的特点,利用单向变分条带去除模型,对VIRR第3通道图像进行条带噪声去除研究,采用噪声去除前后图像行均值曲线与定量评价指标辐射质量改进因子IF (Improvement Factors of Radiometric Quality)和逆变异系数ICV (Inverse Coefficient of Variation)等进行去除结果评价。结果表明,单向变分模型对FY-3C VIRR第3通道观测数据太阳污染条带噪声具有较好的订正效果,实验中,PSNR提升到32.77 dB;在真实数据实验中,IF提升到16.99 dB。
The Visible and Infra-Red Radiometer (VIRR) sensor on FY-3C was affected by the scanning mirror of the remote sensing instrument illuminated by the sunlight at high latitude
which results in the noise of the earth observation image
and the stripe noise of the channel-3 in VIRR
which seriously affected the data application. The causes of strip noise pollution include the influence of direct sunlight near the terminator
the effect of space clamp caused by stray light formed by reflection and scattering of sunlight and the influence of temperature fluctuation of scanning mirror caused by sunlight.
According to the anisotropic characteristics of the stripe noise and the unidirectional variational model
We studied the removal of stripe noise in channel 3 of VIRR
and compared the results with the low-pass filtering method and TV-L1 method. The mean cross-track profiles before and after destriping
Peak Signal to Noise Ratio (PSNR)
Improvement Factors (IF) of radiation quality and Inverse Coefficient of Variation (ICV) were used to evaluate the destriping results. In addition
in order to analyze the change of solar pollution over time
we made pollution line statistics
using the data of January
April
July and October 2014-2019.
The results show that the variational model had a good effect on the stripe noise caused by solar pollution in the observation data of FY-3C VIRR channel 3. In the real experiment
PSNR was increased to 32.77 db; in the real data experiment
IF was increased to 16.99 db. The results of time series analysis of solar pollution show that solar pollution has significant seasonal variation and has significant correlation with satellite
β
angle.
可见光红外扫描辐射计(VIRR)太阳污染变分模型条带噪声FY-3C
Visible and Infra-Red Radiometer(VIRR)solar pollutionvariational modeldestripingFY-3C
Bouali M and Ladjal S. 2011. Toward optimal destriping of MODIS data using a unidirectional variational model. IEEE Transactions on Geoscience and Remote Sensing, 49(8): 2924-2935 [DOI: 10.1109/TGRS.2011.2119399http://dx.doi.org/10.1109/TGRS.2011.2119399]
Cao W F, Chang Y, Han G D and Li J B. 2018. Destriping remote sensing image via low-rank approximation and nonlocal total variation. IEEE Geoscience and Remote Sensing Letters, 15(6): 848-852 [DOI: 10.1109/LGRS.2018.2811468http://dx.doi.org/10.1109/LGRS.2018.2811468]
Chang Y, Yan L X, Fang H Z and Luo C A. 2015. Anisotropic spectral-spatial total variation model for multispectral remote sensing image destriping. IEEE Transactions on Image Processing, 24(6): 1852-1866 [DOI: 10.1109/TIP.2015.2404782http://dx.doi.org/10.1109/TIP.2015.2404782]
Chen J S, Shao Y, Guo H D, Wang W M and Zhu B Q. 2003. Destriping CMODIS data by power filtering. IEEE Transactions on Geoscience and Remote Sensing, 41(9): 2119-2124 [DOI: 10.1109/TGRS.2003.817206http://dx.doi.org/10.1109/TGRS.2003.817206]
Chen J S, Shao Y and Zhu B Q. 2004. Destriping CMODIS Based on FIR Method. Journal of Remote Sensing, 8(3):227-233
陈劲松, 邵芸, 朱博勤. 2004. 中分辨率遥感图像条带噪声的去除. 遥感学报, 8(3):227-233 [DOI: 10.11834/jrs.20040306http://dx.doi.org/10.11834/jrs.20040306]
Di Bisceglie M, Episcopo R, Galdi C and Ullo S L. 2009. Destriping MODIS data using overlapping field-of-view method. IEEE Transactions on Geoscience and Remote Sensing, 47(2): 637-651 [DOI: 10.1109/TGRS.2008.2004034http://dx.doi.org/10.1109/TGRS.2008.2004034]
Huo L J, He B and Zhou D B. 2017. A destriping method with multi-scale variational model for remote sensing images. Optics and Precision Engineering, 25(1): 198-207
霍丽君, 何斌, 周达标. 2017. 遥感图像条带噪声的多尺度变分模型去除. 光学 精密工程, 25(1): 198-207 [DOI: 10.3788/OPE.20172501.0198http://dx.doi.org/10.3788/OPE.20172501.0198]
Ju H H, Liu Z G, Jiang J J and Wang Y. 2018. Removal of hyperspectral stripe noise using low-pass filtered residual images. Acta Optica Sinica, 38(12): 1228002
鞠荟荟, 刘志刚, 姜江军, 汪洋. 2018. 基于低通滤波残差图的高光谱条带噪声去除. 光学学报, 38(12): 1228002 [DOI: 10.3788/aos201838.1228002http://dx.doi.org/10.3788/aos201838.1228002]
Liu X X, Lu X L, Shen H F, Yuan Q Q, Jiao Y L and Zhang L P. 2016. Stripe noise separation and removal in remote sensing images by consideration of the global sparsity and local variational properties. IEEE Transactions on Geoscience And Remote Sensing, 54(5): 3049-3060 [DOI: 10.1109/TGRS.2015.2510418http://dx.doi.org/10.1109/TGRS.2015.2510418]
Liu X X, Shen H F, Yuan Q Q, Lu X L and Zhou C P. 2018. A Universal destriping framework combining 1-D and 2-D variational optimization methods. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 808-822 [DOI: 10.1109/TGRS.2017.2755016http://dx.doi.org/10.1109/TGRS.2017.2755016]
Münch B, Trtik P, Marone F and Stampanoni M. 2009. Stripe and ring artifact removal with combined wavelet-Fourier filtering. Optics Express, 17(10): 8567-8591 [DOI: 10.1364/OE.17.008567http://dx.doi.org/10.1364/OE.17.008567]
Niu X H, Zhou J G, Chen S S, Wang X H, Ding L and Hu X Q. 2015. Simulation and suppression of solar on-orbit pollution of FY-3/MERSI onboard blackbody. Optics and Precision Engineering, 23(7): 1822-1828
钮新华, 周巨广, 陈帅帅, 王向华, 丁雷, 胡秀清. 2015. FY-3/中分辨率光谱成像仪星上黑体的在轨太阳污染模拟与抑制. 光学 精密工程, 23(7): 1822-1828 [DOI: 10.3788/OPE.20152307.1822http://dx.doi.org/10.3788/OPE.20152307.1822]
Rudin L I, Osher S and Fatemi E. 1992. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60(1/4): 259-268 [DOI: 10.1016/0167-2789(92)90242-fhttp://dx.doi.org/10.1016/0167-2789(92)90242-f]
Simpson J J, Stitt J R and Leath D M. 1998. Improved finite impulse response filters for enhanced destriping of geostationary satellite data. Remote Sensing of Environment, 66(3): 235-249 [DOI: 10.1016/s0034-4257(98)00070-4http://dx.doi.org/10.1016/s0034-4257(98)00070-4]
Sun L, Hu X Q, Guo M H and Xu N. 2013. Multisite calibration tracking for FY-3A MERSI solar bands. Advances in Meteorological Science and Technology, 3(4): 84-96
孙凌, 胡秀清, 郭茂华, 徐娜. 2013. 风云三号A星中分辨率光谱成像仪反射太阳波段的多场地定标跟踪. 气象科技进展, 3(4): 84-96 [DOI: 10.3969/j.issn.2095-1973.2013.04.009http://dx.doi.org/10.3969/j.issn.2095-1973.2013.04.009]
Wang M, Huang T Z, Zhao X L, Deng L J and Liu G. 2017. A unidirectional total variation and second-order total variation model for destriping of remote sensing images. Mathematical Problems in Engineering, 2017: 4397189 [DOI: 10.1155/2017/4397189http://dx.doi.org/10.1155/2017/4397189]
Wang M, Zheng X H, Pan J and Wang B. 2016. Unidirectional total variation destriping using difference curvature in MODIS emissive bands. Infrared Physics and Technology, 75: 1-11 [DOI: 10.1016/j.infrared.2015.12.004http://dx.doi.org/10.1016/j.infrared.2015.12.004]
Xu H L, Hu X Q, Xu N and Min M. 2015. Discrimination and correction for solar contamination on mid-infrared band of FY-3C/VIRR. Optics and Precision Engineering, 23(7): 1874-1879
徐寒列, 胡秀清, 徐娜, 闵敏. 2015. FY-3C/可见光红外扫描辐射计中红外通道太阳污染的识别和修正. 光学 精密工程, 23(7): 1874-1879 [DOI: 10.3788/OPE.20152307.1874http://dx.doi.org/10.3788/OPE.20152307.1874]
Yanovsky I and Dragomiretskiy K. 2018. Variational destriping in remote sensing imagery: total variation with L1 fidelity. Remote Sensing, 10(2): 300 [DOI: 10.3390/rs10020300http://dx.doi.org/10.3390/rs10020300]
Yang Z D, Zhang W J, Li J, W. Paul Menzel and Richard A. Frey. 2004. De-striping for MODIS Infrared Band Data via Wavelet Shrinkage. Journal of Remote Sensing, 8(1):23-30
杨忠东, 张文建, 李俊, W. Paul Menzel and Richard A. Frey.2004. 应用小波收缩方法剔除MODIS热红外波段数据条带噪声. 遥感学报, 8(1):23-30 [DOI: 10.11834/jrs.20040104http://dx.doi.org/10.11834/jrs.20040104]
Zhang P, Yang H, Qiu H, Ma G, Yang Z D, Lu N M and Yang J. 2012. Quantitative remote sensing from the current Fengyun 3 satellites. Advances in Meteorological Science and Technology, 2(4): 6-11
张鹏, 杨虎, 邱红, 马刚, 杨忠东, 卢乃锰, 杨军. 2012. 风云三号卫星的定量遥感应用能力. 气象科技进展, 2(4): 6-11 [DOI: 10.3969/j.issn.2095-1973.2012.04.001http://dx.doi.org/10.3969/j.issn.2095-1973.2012.04.001]
Zhang Y Z, Zhou G, Yan L X and Zhang T X. 2016. A destriping algorithm based on TV-Stokes and unidirectional total variation model. Optik, 127(1): 428-439 [DOI: 10.1016/j.ijleo.2015.09.246http://dx.doi.org/10.1016/j.ijleo.2015.09.246]
Zhou G, Fang H Z, Lu C, Wang S Y, Zuo Z Y and Hu J. 2015. Robust destriping of MODIS and hyperspectral data using a hybrid unidirectional total variation model. Optik, 126(7/8): 838-845 [DOI: 10.1016/j.ijleo.2015.02.045http://dx.doi.org/10.1016/j.ijleo.2015.02.045]
Zhou G, Fang H Z, Yan L X, Zhang T X and Hu J. 2014. Removal of stripe noise with spatially adaptive unidirectional total variation. Optik, 125(12): 2756-2762 [DOI: 10.1016/j.ijleo.2013.11.031http://dx.doi.org/10.1016/j.ijleo.2013.11.031]
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