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Sharpening the Pan-Multispectral GF-1 Camera Imagery Using the Gram-Schmidt Approach: The Different Select Methods for Low Resolution Pan in Comparison

  • Qingsheng LiuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

The Gram-Schmidt spectral sharpening is one of the widely used image sharpening techniques with preserving the spectral information of images. It is a key step for selecting methods for simulating the low resolution pan image. In this paper, we compared the different select methods for low resolution pan applied to sharpening the GF-1 multispectral bands. The results indicated that the optimal GS sharpened GF-1 multispectral image was from the low resolution pan simulated from Lansat7 sensor. Although there was no significant difference between the ten sharpened images and the original low resolution multispectral images through visual inspection, the sharpening increase the correlation coefficients between the multispectral bands, which is not beneficial for land use classification. In the future, a comprehensive evaluation over more study areas with more images should be performed.

Keywords

Gram-Schmidt Sharpening GF-1 Low resolution pan 

Notes

Acknowledgment

This research work was jointly financially supported by the National Natural Science Foundation of China (Project No.41671422, 41661144030, 41561144012), the National Mountain Flood Disaster Investigation Project (SHZH-IWHR-57), the Innovation Project of LREIS (Project No.088RA20CYA, 08R8A010YA), and National Key Research and Development Project of China (2016YFC1402701). Thanks to China Center for Resources Satellite Data and Application for providing the GF-1 data.

References

  1. 1.
    Lanaras, C., Bioucas-Dias, J., Baltsavias, E., Schindler, K.: Super-resolution of multispectral multiresolution images from a single sensor. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–28 (2017)Google Scholar
  2. 2.
    Nikolakopoulos, K.G.: Comparison of six fusion techniques for SPOT 5 data. In: IEEE International Geoscience and Remote Sesning Symposium, pp. 2811–2814 (2005)Google Scholar
  3. 3.
    Du, Q., Younan, N.H., King, R., Shah, V.P.: On the performance evaluation of pan-sharpening techniques. IEEE Geosci. Remote Sens. Letters 14, 518–522 (2007)CrossRefGoogle Scholar
  4. 4.
    Nikolakopoulos, K.G.: Comparison of nine fusion techniques for very high resolution data. Photogramm. Eng. Remote Sens. 74, 647–659 (2008)CrossRefGoogle Scholar
  5. 5.
    Zhang, Y., Mishra, P.K.: A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 182–185 (2012)Google Scholar
  6. 6.
    Mandhare, R.A., Upadhyay, P., Gupta, S.: Pixel-level image fusion using Brovey transform and Wavelet transform. Int. J. Adv. Res. Electr. Electr. Instrum. Eng. 2, 2690–2695 (2013)Google Scholar
  7. 7.
    Vivone, G., Alparone, L., Chanussot, J., Mura, M.D., Garzelli, A., Licciardi, G.A., Restaino, R., Wald, L.: A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 33, 2565–2586 (2015)CrossRefGoogle Scholar
  8. 8.
    Nikolakopoulos, K.G., Oikonomidis, D.: Quality assessment of ten fusion techniques applied on worldview-2. Eur. J. Remote Sens. 48, 141–167 (2015)CrossRefGoogle Scholar
  9. 9.
    Maglione, P., Parente, C., Vallario, A.: Pan-sharpening worldview-2: IHS, Brovey and Zhang methos in comparison. Int. J. Eng. Technol. 8, 673–679 (2016)Google Scholar
  10. 10.
    Li, H., Jing, L.H., Tang, Y.W.: Assessment of pansharpening methods applied to worldview-2 imagery fusion. Sensors 17, 89 (2017)CrossRefGoogle Scholar
  11. 11.
    Pak, H., Choi, J., Choi, S.: Sharpening the VNIR and SWIR bands of Sentinel-2A imagery through modified selected and synthesized band schemes. Remote Sens. 9, 1080 (2017)CrossRefGoogle Scholar
  12. 12.
    Liu, Q.S.: Sharpening the WBSI Imagery of Tiangong-II: gram-schmidt and principal components transform in comparison. In: 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 524–531 (2018)Google Scholar
  13. 13.
    Cresda, GF-1, Slate, http://www.cresda.com/EN/satellite/7155.shtml. Accessed 3 Jan 2019
  14. 14.
    Barbier, N., Couteron, P., Lejoly, J., Deblauwe, V., Lejeune, O.: Self-organized vegetation patterning as a fingerprint of climate and human impact on semi-arid ecosystems. J. Ecol. 94, 537–547 (2006)CrossRefGoogle Scholar
  15. 15.
    Liu, Q.S., Huang, C., Liu, G.H., Yu, B.W.: Comparison of CBERS-04, GF-1, and GF-2 satellite panchromatic images for mapping quasi-circular vegetation patches in the yellow river delta. China Sens. 18, 2733 (2018)CrossRefGoogle Scholar
  16. 16.
    Sarp, G.: Spectral and spatial quality analysis of pan sharpening algorithms: a case study in Istanbul. Eur. J. Remote Sens. 47, 19–28 (2014)CrossRefGoogle Scholar
  17. 17.
    Gao, L., Wang. Z.W., Jiang, M.: Assessment of fusion methods of ZY-3 satellite images. In: 2016 Academic Annual Conference of Jiansu Provincial Society for Surveying, Mapping and Geoinformation, pp. 44–46 (2016, In Chinese)Google Scholar
  18. 18.
    Liu, Q.S., Li, X.Y., Liu, G.H., Huang, C., Li, H., Guan, X.D.: Sharpening of the VNIR and SWIR bands of the wide band spectral imager onboard Tiangong-II imagery using the selected bands. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-3, ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing”, pp. 1085–1092 (2018)Google Scholar
  19. 19.
    Liu, Q.S.: Sharpening the VNIR-SWIR-TIR bands of the WIS of Tiangong-2 for mapping land use and land cover. In: Gu, Y., Gao., M., Zhao, G. (eds.) Proceedings of the Tiangong-2 Remote Sensing Application Conference-Technology, Method and Application 2018, Lecture Notes in Electrical Engineering, vol. 541, pp. 212–221. Springer, Singapore (2019)Google Scholar
  20. 20.
    Liu, Q.S.: Comparing the different seasonal CBERS 04 images to map the quasi-circular vegetation patches in the yellow river delta, China. In: 2018 International Conference on Sensor Networks and Signal Processing (SNSP 2018) (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina

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