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Research into the Adaptability Evaluation of the Remote Sensing Image Fusion Method Based on Nearest-Neighbor Diffusion Pan Sharpening

  • Chunyang Wang
  • Weikuan Shao
  • Huimin Lu
  • Hebing Zhang
  • Shuangting Wang
  • Handong Yue
Conference paper
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Nearest-neighbor diffusion pan sharpening, as a new image fusion method based on nearest-neighbor diffusion, has become a new hot spot of research. In this paper, the nearest-neighbor diffusion pan sharpening method is used for a WorldView-2 image fusion experiment and compared with the methods we usually use such as the wavelet transform fusion method, the PCA transform fusion method, and the Gram–Schmidt transform fusion method. The experimental results show that the spatial information is better than the other three methods in terms of spatial details and texture.

Keywords

Image fusion WorldView-2 Nearest-neighbor diffusion pan sharpening Wavelet transform PCA transform Gram–Schmidt transform 

Notes

Acknowledgments

This research is supported by the key research project fund of the Institution of Higher Education in Henan Province (18A420001), the Henan Polytechnic University Doctoral Fund (B2016-13), and The Open Program of the Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains, Henan Province (2016A002).

References

  1. 1.
    Liu, Z., Blasch, E., & John, V. (2017). Statistical comparison of image fusion algorithms: Recommendations. Information Fusion, 36, 251–260.CrossRefGoogle Scholar
  2. 2.
    Huimin, L., Li, Y., Shota, N., Hyongseop, K., & Seiichi, S. (2013). Principles and methods of remote sensing application analysis. Beijing: Science Press.Google Scholar
  3. 3.
    Chen, C., Qin, Q., Wang, J., et al. (2011). Comparison of quality evaluation methods for image fusion of farmland remote sensing. Transactions of the CSAE, 27(10), 95–100.Google Scholar
  4. 4.
    Wang, L., Niu, X., Wei, B., et al. (2015). Study on quality evaluation methods for remotely sensed images fusion. Bulletin of Surveying and Mapping, 2, 77–79.Google Scholar
  5. 5.
    Li, Y., Lu, H., Li, J., et al. (2016). Underwater image de-scattering and classification by deep neural network. Computers and Electrical Engineering, 54, 68–77.CrossRefGoogle Scholar
  6. 6.
    Lu, H., Li, Y., Nakashima, S., et al. (2016). Turbidity underwater image restoration using spectral properties and light compensation. IEICE Transactions on Information and Systems, 99(1), 219–227.CrossRefGoogle Scholar
  7. 7.
    Lu, H., Li, Y., Zhang, L., & Serikawa, S. (2015). Contrast enhancement for images in turbid water. Journal of the Optical Society of America A, 32(5), 886–893.CrossRefGoogle Scholar
  8. 8.
    Lu, H., Li, Y., Zhang, Y., et al. (2017). Underwater optical image processing: A comprehensive review. Mobile Networks and Applications, 22(6), 1204–1211.CrossRefGoogle Scholar
  9. 9.
    Chen, M., Hao, Y., Qiu, M., et al. (2016). Mobility-aware caching and computation offloading in 5G ultra-dense cellular networks. Sensors, 16, 974.CrossRefGoogle Scholar
  10. 10.
    Chen, M., Yang, J., Hao, Y., et al. (2017). A 5G cognitive system for healthcare. Big Data and Cognitive Computing, 1, 2.  https://doi.org/10.3390/bdcc1010002.CrossRefGoogle Scholar
  11. 11.
    Chen, M., Shi, X., Zhang, Y., et al. (2017). Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data.  https://doi.org/10.1109/TBDATA.2017.2717439
  12. 12.
    Sun, W., & Messinger, D. (2014). Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Optical Engineering, 53(1), 013107.CrossRefGoogle Scholar
  13. 13.
    Shannon, C. E. (2014). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423.MathSciNetCrossRefGoogle Scholar
  14. 14.
    Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. The American Statistician, 42(1), 59–66.CrossRefGoogle Scholar
  15. 15.
    Schwartz, M. H., & Rozumalski, A. (2008). The gait deviation index: A new comprehensive index of gait pathology. Gait and Posture, 28(3), 351–357.CrossRefGoogle Scholar
  16. 16.
    Bennis, D., Garcia Rozas, J. R., & Oyonarte, L. (2016). Relative Gorenstein global dimension. International Journal of Algebra and Computation, 26(8), 1597–1615.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Chunyang Wang
    • 1
  • Weikuan Shao
    • 1
  • Huimin Lu
    • 2
  • Hebing Zhang
    • 1
  • Shuangting Wang
    • 1
  • Handong Yue
    • 1
  1. 1.School of Surveying and Land Information EngineeringHenan Polytechnic UniversityJiaozuoChina
  2. 2.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyKitakyushuJapan

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