Study on the correction of sunlight pollution in mid-infrared image of FY-3C/VIRR

  • role: First author第一作者
  • Affiliation:

    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

  • Email:zjibiao9701@foxmail.com
  • Introduction:1995E-mailzjibiao9701@foxmail.com
ZHU Jibiao1,  
  • Affiliation:

    National Satellite Meteorological Centre, Beijing 100081, China

HU Xiuqing2,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China

  • Email:yanglk@hpu.edu.cn
  • Introduction:,1980E-mailyanglk@hpu.edu.cn
YANG Leiku1*,  
  • Affiliation:

    National Satellite Meteorological Centre, Beijing 100081, China

XU Hanlie2,  
  • Affiliation:

    National Satellite Meteorological Centre, Beijing 100081, China

XU Na2,  
  • Affiliation:

    National Satellite Meteorological Centre, Beijing 100081, China

ZHANG Peng2

resumen

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.

palabra clave

visible and infra-red radiometer(VIRR);solar pollution;variational model;destriping;FY-3C

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Chen J S, Shao Y and Zhu B Q. 2004. Destriping CMODIS Based on FIR Method. Journal of Remote Sensing, 8(3):227-233
  6. 6.
    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
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    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
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Yanovsky I and Dragomiretskiy K. 2018. Variational destriping in remote sensing imagery: total variation with L1 fidelity. Remote Sensing, 10(2): 300
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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

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