Skip to main content
SpringerLink
Log in

Conventional Image Fusion Approaches in Remote Sensing

  • Chapter
Image Fusion in Remote Sensing

  • 109 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 19.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 29.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G. A., Restaino, R., and Wald, L. 2014. A critical comparison among pan-sharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53(5):2565–2586. DOI: https://doi.org/10.1109/tgrs.2014.2361734. 15, 16, 19, 21, 22, 23, 26

    Article  Google Scholar 

  2. Rahmani, S., Strait, M., Merkurjev, D., Moeller, M., and Wittman, T. 2010. An adaptive IHS pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 7(4):746–750. DOI: https://doi.org/10.1109/lgrs.2010.2046715. 15

    Article  Google Scholar 

  3. Aiazzi, B., Alparone, L., Baronti, S., Carl`a, R., Garzelli, A., and Santurri, L. 2016. Sensitivity of pan-sharpening methods to temporal and instrumental changes between multispectral and panchromatic data sets. IEEE Transactions on Geoscience and Remote Sensing, 55(1):308–319. DOI: https://doi.org/10.1109/tgrs.2016.2606324. 15

    Article  Google Scholar 

  4. 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. DOI: https://doi.org/10.1016/s1566-2535(01)00036-7. 15, 16, 24

    Article  Google Scholar 

  5. Tu, T.-M., Huang, P. S., Hung, C.-L., and Chang, C.-P. 2004. A fast intensityhuesaturation fusion technique with spectral adjustment for IKONOS imagery. IEEE Geoscience and Remote Sensing Letters, 1(4):309–312. DOI: https://doi.org/10.1109/lgrs.2004.834804. 15, 16

    Article  Google Scholar 

  6. Aiazzi, B., Baronti, S., and Selva, M. 2007. Improving component substitution pansharpening through multivariate regression of MS+Pan data. IEEE Transactions on Geoscience and Remote Sensing, 45(10):3230–3239. DOI: https://doi.org/10.1109/tgrs.2007.901007. 15, 16

    Article  Google Scholar 

  7. Azarang, A. and Kehtarnavaz, N. 2020. Multispectral image fusion based on map estimation with improved detail. Remote Sensing Letters, 11(8):797–806. DOI: https://doi.org/10.1080/2150704x.2020.1773004. 16

    Article  Google Scholar 

  8. Dou, W., Chen, Y., Li, X., and Sui, D. 2007. A general framework for component substitution image fusion: An implementation using fast image fusion method. Computers and Geoscience, 33(2):219–228. DOI: https://doi.org/10.1016/j.cageo.2006.06.008. 16

    Article  Google Scholar 

  9. Gillespie, A. R., Kahle, A. B., and Walker, R. E. 1987. Color enhancement of highly correlated images—II. Channel ratio and Chromaticity, trans-form techniques. Remote Sensing of Environment, 22(3):343–365. DOI: https://doi.org/10.1016/0034-4257(87)90088-5. 17

    Article  Google Scholar 

  10. Amro, I., Mateos, J., Vega, M., Molina, R., and Katsaggelos, A. K. 2011. A survey of classical methods and new trends in pan-sharpening of multispectral images. EURASIP Journal on Advances in Signal Processing, 2011(1):79:1–79:22. DOI: https://doi.org/10.1186/1687-6180-2011-79. 17

    Article  Google Scholar 

  11. Aiazzi, B., Alparone, L., Baronti, S., Garzelli, A., and Selva, M. 2012. Twenty-five years of pan-sharpening: A critical review and new developments. Signal and Image Processing for Remote Sensing, 2nd ed., Chen, C.-H., Ed. Boca Raton, FL, CRC Press, pages 533–548. DOI: https://doi.org/10.1201/b11656-30. 17

    Chapter  Google Scholar 

  12. Baronti, S., Aiazzi, B., Selva, M., Garzelli, A., and Alparone, L. 2011. A theoretical analysis of the effects of aliasing and misregistration on pansharpened imagery. IEEE Journal of Select Topics in Signal Processing, 5(3):446–453. DOI: https://doi.org/10.1109/jstsp.2011.2104938. 17

    Article  Google Scholar 

  13. Laben, C. A. and Brower, B. V. 2000. Process for enhancing the spatial resolution of multispectral imagery using pan-sharpening. U.S. Patent6 011 875. 17

    Google Scholar 

  14. Rahmani, S., Strait, M., Merkurjev, D., Moeller, M., and Wittman, T. 2010. An adaptive IHS pan-sharpening method. IEEE Geoscience and Remote Sensing Letters, 7(4):746–750. DOI: https://doi.org/10.1109/lgrs.2010.2046715. 17, 18, 22

    Article  Google Scholar 

  15. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6):679–698. DOI: https://doi.org/10.1109/tpami.1986.4767851. 18

    Article  Google Scholar 

  16. Perona, P. and Malik, J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7):629–639. DOI: https://doi.org/10.1109/34.56205. 18

    Article  Google Scholar 

  17. Azarang, A. and Ghassemian, H. 2017. An adaptive multispectral image fusion using particle swarm optimization. Iranian Conference on Electrical Engineering (ICEE), IEEE, pages 1708–1712. DOI: https://doi.org/10.1109/iraniancee.2017.7985325. 20

  18. Khademi, G. and Ghassemian, H. 2017. A multi-objective component-substitutionbased pan-sharpening. 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE, pages 248–252. DOI: https://doi.org/10.1109/pria.2017.7983056. 20

  19. Garzelli, A., Nencini, F., and Capobianco, L. 2008. Optimal MMSE pan sharpening of very high resolution multispectral images. IEEE Transactions on Geoscience Remote Sensing, 46(1):228–236. DOI: https://doi.org/10.1109/tgrs.2007.907604. 20

    Article  Google Scholar 

  20. Azarang, A. and Kehtarnavaz, N. 2020. Multispectral image fusion based on map estimation with improved detail. Remote Sensing Letters, 11(8):797–806. DOI: https://doi.org/10.1080/2150704x.2020.1773004. 22

    Article  Google Scholar 

  21. Ranchin, T. and Wald, L. 2000. Fusion of high spatial and spectral resolution images: The ARSIS concept and its implementation. Photogrammatic Engineering and Remote Sensing, 66(1):49–61. 24, 27

    Google Scholar 

  22. Khan, M. M., Chanussot, J., Condat, L., and Montavert, A. 2008. Indusion: Fusion of multispectral and panchromatic images using the induction scaling technique. IEEE Geoscience and Remote Sensing Letters, 5(1):98–102. DOI: https://doi.org/10.1109/lgrs.2007.909934. 25

    Article  Google Scholar 

  23. Burt, P. J. and Adelson, E. H. 1983. The Laplacian pyramid as a compact image code. IEEE Transactions on Communications, COM-31(4):532–540. DOI: https://doi.org/10.1109/tcom.1983.1095851. 26

    Article  Google Scholar 

  24. Shensa, M. J. 1992. The discrete wavelet transform: Wedding the à trousand Mallat algorithm. IEEE Transactions on Signal Processing, 40(10):2464–2482. DOI: https://doi.org/10.1109/78.157290. 26

    Article  Google Scholar 

  25. González-Audícana, M., Otazu, X., Fors, O., and Seco, A. 2005. Comparison between Mallat’s and the “`a trous” discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images. International Journal of Remote Sensing, 26(3):595–614. DOI: https://doi.org/10.1080/01431160512331314056. 26

    Article  Google Scholar 

  26. Vetterli, M. and Kovacevic, J. 1995. Wavelets and Subband Coding, Englewood Cliffs, NJ, Prentice Hall. 26

    Google Scholar 

  27. Núñezet, J. et al. 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Transactions on Geoscience and Remote Sensing, 37(3):1204–1211. DOI: https://doi.org/10.1109/36.763274. 27

    Article  Google Scholar 

  28. Vivone, G., Restaino, R., Dalla Mura, M., Licciardi, G., and Chanussot, J. 2014. Contrast and error-based fusion schemes for multispectral image pan-sharpening. IEEE Geoscience and Remote Sensing Letters, 11(5):930–934. DOI: https://doi.org/10.1109/lgrs.2013.2281996. 27

    Article  Google Scholar 

  29. Otazu, X., González-Audícana, M., Fors, O., and Núñez, J. 2005. Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods. IEEE Transactions on Geoscience and Remote Sensing, 43(10):2376–2385. DOI: https://doi.org/10.1109/tgrs.2005.856106. 27

    Article  Google Scholar 

  30. Ulfarsson, M. O., Palsson, F., Mura, M. D., and Sveinsson, J. R. Sentinel-2 sharpening using a reduced-rank method. IEEE Transactions on Geoscience and Remote Sensing, to be published. DOI: https://doi.org/10.1109/tgrs.2019.2906048. 27

Download references

Author information

Authors and Affiliations

  1. The University of Texas at Dallas, Richardson, Texas, USA

    Arian Azarang & Nasser Kehtarnavaz

Authors
  1. Arian Azarang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nasser Kehtarnavaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Cite this chapter

Azarang, A., Kehtarnavaz, N. (2021). Conventional Image Fusion Approaches in Remote Sensing. In: Image Fusion in Remote Sensing. Synthesis Lectures on Image, Video, and Multimedia Processing. Springer, Cham. https://doi.org/10.1007/978-3-031-02256-2_3

Download citation

Publish with us

Policies and ethics

Search

Navigation