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Research on Infrared Image Quality Improvement Based on Ground Compensation

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Part of the book series: Springer Proceedings in Physics ((SPPHY,volume 232))

Abstract

Due to the non-linear response of the infrared load device in the low-temperature region, the stripes, appeared in the low-temperature region of the infrared image, will seriously deteriorate the image quality and quantitative retrieval accuracy of the infrared remote sensing image. The existing methods based on the calibration parameters on the infrared remote sensing satellite can’t solve the stripe problem caused by the non-linear response. Therefore, the compensation by means of ground to improve the quality of infrared images is particularly urgent. This paper deeply analysed the generation mechanism of satellite infrared camera load inhomogeneity and studied two improved stripe removal algorithms based on Kalman filtering and moment matching in view of the shortcomings of the traditional method in removing stripes. First of all, this paper introduces the advantages of Kalman filtering noise processing and the deficiencies of the algorithm in image processing. Then, a method based on Kalman filtering is proposed to improve the radiation quality of infrared images. Secondly, aiming at the problem of “Banding effect” in traditional moment matching, a one-dimensional moving window was proposed to segment the image to protect the image detail information, and we also propose a method to enhance the radiation quality of moment matched infrared images based on adaptive moving window weighted column average compensation. Through the actual data processing, the improved two methods could effectively eliminate the stripes in the image, improve the target temperature inversion accuracy in a variety of complex scenes, and enhance the visual effect of the image. Moreover, the accuracy of temperature inversion of targets in various complex scenarios is also improved, which will greatly improve the quantitative availability of infrared remote sensing data.

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References

  1. Liu, L., Chen, Y., Li, J.: Overview and prospect of remote sensing image texture analysis methods. Remote Sens. Technol. Appl. 6(18), 441–447 (2003)

    Google Scholar 

  2. Li, Q., Zhou, H., Liu, S.: Infrared focal plane array non-uniformity correction algorithm based on wavelet transform. Acta Opt. Sin. 9(27), 1617–1620 (2007)

    Google Scholar 

  3. Hong, W., Yang, N., Wang, X.: Infrared focal plane joint non-uniformity correction algorithm. Acta Opt. Sin. 6(31), 81–86 (2011)

    Google Scholar 

  4. Wang, S.P.: Stripe noise removal for infrared image by minimizing difference between columns. Infrared Phys. Technol. 77(6), 58–64 (2016)

    Article  ADS  Google Scholar 

  5. Giovani, C., Marco, D., Thomas, W.: Striping removal in MOS-B data. IEEE Trans. Geosci. Remote Sens. 3(38) (2000)

    Google Scholar 

  6. Zhu, Y., Gong, C., Hu, Y., Zhu, L., He, H.: Moment matching and Kalman filtering in infrared image non-uniformity correction applications. Infrared Technol. 35(11) (2013)

    Google Scholar 

  7. Torres, S.N., Pezoa, J.E., Hayat, M.M.: Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form. Appl. Opt. 42(29), 5872–5881 (2003)

    Article  ADS  Google Scholar 

  8. Zhang, S., Xiang, W., Xu, B., Feng, B.: Stripe noise removal for infrared images using guided filter. Infrared Phys. Technol. 77(6), 58–64 (2016)

    ADS  Google Scholar 

  9. Chang, Y., Yan, L., Wu, T., Zhong, S.: Remote sensing image stripe noise removal: from image decomposition perspective. IEEE Trans. Geosci. Remote Sens. 12(54), 85 (2016)

    Google Scholar 

  10. Qin, Y., Deng, R., He, Y.: Piecewise linear dynamic moment matching strip removal. J. Image Graph. 11(17), 1444–1452 (2012)

    Google Scholar 

  11. Song, B.: Research on Strip Removal Method of Remote Sensing Image, pp. 17–18. XiDian University, Xi’an (2013)

    Google Scholar 

  12. He, L., Pan, Q., Zhao, Y.: Hyperspectral image small target detector based on single likelihood test. Acta Opt. Sin. 12(27), 2154–2162 (2007)

    Google Scholar 

  13. Wegener, M.: Destriping multiple sensor imagery by improved histogram matching. Int. J. Remote Sens. 11(5), 859–875 (1990)

    Article  ADS  Google Scholar 

  14. Zhang, L., Tao, H., Liu, G., Jiang, T.: Nonlinear rational approximation model for geometric correction of remote sensing images. J. China Coal Soc. 2(28), 140–144 (2003)

    Google Scholar 

  15. Yang, B., Wang, B.: A survey of nonlinear dispersion of hyperspectral remote sensing images. J. Infrared Millimeter Waves. 2(36), 173–185 (2017)

    Google Scholar 

  16. Liang, J., Zhou, W., Chuan, C., Dong, Y.: Research on extraction of textured statistical information from remote sensing image based on sliding window method. J. Anhui Agric. Sci. 15(36), 6426–6428 (2008)

    Google Scholar 

  17. Xiong, T.: Mining Multivariate Time Series Data Dynamic Association Rules Based on Sliding Window. Harbin Institute of Technology, Harbin (2016)

    Google Scholar 

  18. Zhang, B., Wang, M., Pan, J.: Adaptive moment match stripe noise removal method using grayscale segmentation. Geomat. Inform. Sci. Wuhan Univ. 12(37), 1464–1467 (2012)

    Google Scholar 

  19. Amraei, E., Mobasheri, M.R.: Stripe noise removal of images acquired by CBERS 2 CCD camera sensors. In: The 1st ISPRS International Conference on Geospatial Information Research, pp. 15–17 (2014)

    Google Scholar 

  20. Fang, C., Zhao, Y.: Hyperspectral image stripe noise removal algorithm. Comput. Eng. Appl. 12(48), 158–162 (2012)

    Google Scholar 

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Correspondence to Xiaofei Qu .

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Qu, X., Lin, X., Li, M. (2020). Research on Infrared Image Quality Improvement Based on Ground Compensation. In: Urbach, H., Yu, Q. (eds) 5th International Symposium of Space Optical Instruments and Applications. ISSOIA 2018. Springer Proceedings in Physics, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-030-27300-2_34

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