предыдущий|следующий

A MVG-based non-reference quality evaluation method for Pan/MS Fusion

  • role： First author第一作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：a13726327238@163.com
  • Introduction：包科迪，研究方向为数字图像处理及图像质量评价。E-mail：a13726327238@163.com
BAO Kedi,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

  • Email：mengxiangchao@nbu.edu.cn
  • Introduction：孟祥超，研究方向为多源遥感数据融合与应用。E-mail：mengxiangchao@nbu.edu.cn
MENG Xiangchao*,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

SHAO Feng,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

YE Mengmeng,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

JIN Kangjun,  
  • Affiliation：

    Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

PENG Zhenyu

реферат

Pansharpening aims to sharpen a Low-spatial-Resolution (LR) multispectral (MS) image using a High-spatial-Resolution (HR) panchromatic (PAN) image to obtain a HR MS image. The pansharpened image with high spatial resolution and spectral fidelity had a wide application prospect. However, on the one hand, pansharpening methods usually produce different spatial or spectral distortions; on the other hand, the real HR MS reference image for the quality evaluation of the fused image cannot be obtained due to the limitation of satellite imaging systems. Therefore, the effective evaluation the quality of the fusion image without the real reference image is of great significance.In this paper, we propose a new non-reference quality evaluation method for pansharpened image on the basis of the Multivariate Gaussian Model (MVG). On the basis of a large-scale pansharpening database, this study constructed benchmark, test, and verification data sets, including various satellite sensors and thematic types. Then, a novel benchmark MVG evaluation model was constructed on the basis of the benchmark data set. In the proposed method, the images were first divided into sub-image blocks, and the spatial and spectral sensitive features of each sub-image block were first extracted. Then, many spatial and spectral characteristics were trained to establish the benchmark MVG evaluation model. In addition, in the benchmark MVG training, the sub-image with a high variance was selected for model training to enhance the robustness of the benchmark MVG model. Then, the testing MVG evaluation model for the fused image was established by comprehensively considering the spatial and spectral distortions. Finally, the relative distance between the benchmark and testing MVG models of the fused image was used to calculate the fused image quality.Experimental results show that the proposed method has better performance than the traditional non-reference quality evaluation methods in most satellite- and thematic-based data sets. The proposed method is based on Wald’s protocol and cannot be extended well to the full resolution evaluation of the fused image. Therefore, in future work, we will continue to study the full resolution evaluation method for pansharpening, which is more challenging and meaningful.

ключеви́че слова́

remote sensing;image fusion;non-reference quality evaluation;Multivariate Gaussian Model (MVG);panchromatic image;multispectral image

References

  1. 1.
    Aiazzi B, Alparone L, Baronti S and Garzelli A. 2002. Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40(10): 2300-2312
  2. 2.
    Aiazzi B, Alparone L, Baronti S, Garzelli A and Selva M. 2006. MTF-tailored multiscale fusion of high-resolution MS and pan imagery. Photogrammetric Engineering and Remote Sensing, 72(5): 591-596
  3. 3.
    Alparone L, Aiazzi B, Baronti S, Garzelli A, Nencini F and Selva M. 2008. Multispectral and panchromatic data fusion assessment without reference. Photogrammetric Engineering and Remote Sensing, 74(2): 193-200
  4. 4.
    Alparone L, Baronti S, Garzelli A and Nencini F. 2004. A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1(4): 313-317
  5. 5.
    Bishop C M. 2006. Pattern Recognition and Machine Learning. New York: Springer
  6. 6.
    Canny J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6): 679-698
  7. 7.
    Chavez P S, Sides S C and Anderson J A. 1991. Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3): 295-303
  8. 8.
    Fukunaga K. 1972. Introduction to Statistical Pattern Recognition. New York, NY, USA: Academic Press
  9. 9.
    Garzelli A and Nencini F. 2009. Hypercomplex quality assessment of multi/hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 6(4): 662-665
  10. 10.
    Garzelli A, Nencini F and Capobianco L. 2008. Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(1): 228-236
  11. 11.
    Han P, Gong J Y, Li Z L and Cheng L. 2008. Comparing the effect of aggregation methods for remote sensing image. Journal of Remote Sensing, 12(6): 964-971
  12. 12.
    Kwan C, Budavari B, Bovik A C and Marchisio G. 2017. Blind quality assessment of fused WorldView-3 images by using the combinations of pansharpening and hypersharpening paradigms. IEEE Geoscience and Remote Sensing Letters, 14(10): 1835-1839
  13. 13.
    Laben C A and Brower B V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S., No. 6011875
  14. 14.
    Meng X C, Shen H F, Yuan Q Q, Li H F, Zhang L P and Sun W W. 2019. Pansharpening for cloud-contaminated very high-resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(5): 2840-2854
  15. 15.
    Meng X C, Xiong Y M, Shao F, Shen H F, Sun W W, Yang G, Yuan Q Q, Fu R D and Zhang H Y. 2021. A large-scale benchmark data set for evaluating pansharpening performance: overview and implementation. IEEE Geoscience and Remote Sensing Magazine, 9(1): 18-52
  16. 16.
    Ojala T, Pietikainen M and Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971-987
  17. 17.
    Ranchin T and Wald L. 2000. Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation. Photogrammetric Engineering and Remote Sensing, 66(1): 49-61
  18. 18.
    Sales M H R, Souza C M and Kyriakidis P C. 2013. Fusion of MODIS images using Kriging with external drift. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2250-2259
  19. 19.
    Sheikh H R, Sabir M F and Bovik A C. 2006. A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Transactions on Image Processing, 15(11): 3440-3451
  20. 20.
    Tu T M, Su S C, Shyu H C and Huang P S. 2001. A new look at IHS-like image fusion methods. Information Fusion, 2(3): 177-186
  21. 21.
    Vivone G, Alparone L, Chanussot J, Mura M D, Garzelli A, Licciardi G A, Restaino R and Wald L. 2015. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2565-2586
  22. 22.
    Vivone G, Restaino R and Chanussot J. 2018. A Bayesian procedure for full-resolution quality assessment of pansharpened products. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4820-4834
  23. 23.
    Wald L. 2000. Quality of high resolution synthesised images: is there a simple criterion?//Proceedings of the Third Conference. Nice, France: SEE/URISCA: 99-103
  24. 24.
    Wald L, Ranchin T and Mangolini M. 1997. Fusion of satellite images of different spatial resolutions: assessing the quality of resulting images. Photogrammetric Engineering and Remote Sensing, 63(6): 691-699
  25. 25.
    Wang Z and Bovik A C. 2002. A universal image quality index. IEEE Signal Processing Letters, 9(3): 81-84
  26. 26.
    Wang Z, Bovik A C, Sheikh H R and Simoncelli E P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4): 600-612
  27. 27.
    Xiong Y M, Shao F, Meng X C, Jiang Q P, Sun W W, Fu R D and Ho Y S. 2020. A large-scale remote sensing database for subjective and objective quality assessment of pansharpened images. Journal of Visual Communication and Image Representation, 73: 102947
  28. 28.
    Yang J X, Zhao Y Q, Yi C and Chan J C W. 2017. No-reference hyperspectral image quality assessment via quality-sensitive features learning. Remote Sensing, 9(4): 305
  29. 29.
    Yuhas R H, Goetz A F H and Boardman J W. 1992. Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm//Summaries of the Third Annual JPL Airborne Geoscience Workshop. [s.l.]: JPL: 147-149
  30. 30.
    Zhang L F, Peng M Y, Sun X J, Cen Y and Tong Q X. 2019. Progress and bibliometric analysis of remote sensing data fusion methods (1992-2018). Journal of Remote Sensing, 23(4): 603-619
  31. 31.
    Zhang L P and Shen H F. 2016. Progress and future of remote sensing data fusion. Journal of Remote Sensing, 20(5): 1050-1061
  32. 32.
    Zheng J, Wang J and Song S M. 2019. Advantages of Canny double threshold operator in edge detection. Geospatial Information, 17(11): 128-130
  33. 33.
    Zhou B Z, Shao F, Meng X C, Fu R D and Ho Y S. 2019. No-reference quality assessment for pansharpened images via opinion-unaware learning. IEEE Access, 7: 40388-40401

Читать полностью

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website