Spatial Information Research

, Volume 27, Issue 1, pp 1–9 | Cite as

Performance evaluation of image fusion techniques for Indian remote sensing satellite data using Z-test

  • Vijay SolankyEmail author
  • Kandrika Sreenivas
  • Sunil Kumar Katiyar


Image fusion is being used since last two to three decades in remote sensing for improving visual appearance of coarse resolution imagery using fine spatial resolution data. The resultant outputs are being used successfully in various applications such as image classification, feature extraction, digital change detection, and many more including multi-temporal and multi-scale change detection. Acceptability of a fusion method for a particular application depends upon various factors; one of them is quality of fused image. In this research paper the four different image fusion techniques namely Ehlers, IHS fusion, Brovey and FuzeGo have been evaluated using IRS-P6 (Cartosat-1) and RESOURCESAT-2 (LISS-IV) images of Bhopal city, India. Quality of fusion results is assessed by performing visual analysis between fused image and multispectral (MS) image along with statistical analysis. Visual comparison is done based on better visibility of different land cover features such as roads, buildings, water body and sharpness of edges present in image. For statistical evaluation of fusion process, six statistical parameters i.e. standard deviation (SD), correlation coefficient (CC), entropy/noise, RMSE and ERGAS have been used. In addition to these traditional statistical measures, Z-test is used for combined assessment of fusion techniques. Visual comparisons of fusion results obtained for test site have shown that FuzeGo algorithm has given comparatively better results than other algorithms. Statistical parameter CC, SD are found highest for IHS method, RMSE, and ERGAS are found highest for Brovey method and least noise is added by FuzeGo algorithm. Overall visual analysis and Z-test indicates that FuzeGo has given better results which are followed by Ehlers as compare to other methods.


Image fusion IRS Z-test Quality assessment 


  1. 1.
    Pohl, C., & Van Genderen, (1998). Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823–854. Scholar
  2. 2.
    Aiazzi, B., Alparone, L., Baronti, S., & Garzelli, A. (2002). Context-driven fusion of high spatial and spectral resolution images based on oversampled multi-resolution analysis. IEEE Transactions on Geoscience and Remote Sensing, 40(10), 2300–2312.CrossRefGoogle Scholar
  3. 3.
    Solanky, V., & Katiyar, S. K. (2016). Pixel-level image fusion techniques in remote sensing: A review. Spatial Information Research, 24, 475. Scholar
  4. 4.
    Zhang, Y., & Mishra, R. (2012). A review and comparison of commercially available pan- sharpening techniques for high resolution satellite image fusion. In Proceeding. IEEE international geoscience and remote sensing symposium (IGARSS) (pp. 182–185).Google Scholar
  5. 5.
    Ehlers, M., Klonus, S., Johan Åstrand, P., & Rosso, P. (2010). Multi-sensor image fusion for pansharpening in remote sensing. International Journal of Image and Data Fusion, 1(1), 25–45. Scholar
  6. 6.
    Zhang, Y. (1999). A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20(10), 2003–2014.CrossRefGoogle Scholar
  7. 7.
    Pohl, C. (2016). Multisensor image fusion guidelines in remote sensing. In IOP conference series: Earth and environmental science (EES) Vol. 34 conference 1.Google Scholar
  8. 8.
    Wald, L., Ranchin, T., & 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.Google Scholar
  9. 9.
    Alparone, L., Aiazzi, B., Baronti, S., & Garzelli, A. (2015). Remote sensing image fusion., Signal and image processing of earth observations series Boca Raton: CRC Press Taylor & Francis Group. ISBN 978-1-4665-8750-2.CrossRefGoogle Scholar
  10. 10.
    Hallada, W.A., & Cox, S. (1983). Image sharpening for mixed spatial and spectral resolution satellite systems. In Proceedings of the 17th international symposium on remote sensing of environment, (pp. 1023–1032). Environmental Research Institute Michigan Ann Arbor, MI.Google Scholar
  11. 11.
    Ehlers, M. (2004). Spectral characteristics preserving image fusion based on Fourier domain filtering. In Proceedings of SPIE, Maspalomas, Spain vol. 5574, (pp. 1–13).Google Scholar
  12. 12.
    Zhang, Y. (2004). System and method for image fusion, United States Patent, Patent no. US 2004/0141659 A1.Google Scholar
  13. 13.
    Sasikala, M., & Kumaravel, N. (2007). A comparative analysis of feature based image fusion methods. Information Technology Journal, 6(8), 1224–1230.CrossRefGoogle Scholar
  14. 14.
    Karathanassi, V., Kolokousis, P., & Ioannidou, S. (2007). A comparison study on fusion methods using evaluation indicators. International Journal of Remote Sensing, 28(10), 2309–2341.CrossRefGoogle Scholar
  15. 15.
    Alparone, L., Baronti, S., Garzelli, A., & Nencini, F. (2004). A global quality measurement of pan-sharpened multispectral imagery. IEEE Geoscience and Remote Sensing Letters, 1(4), 313–317.CrossRefGoogle Scholar
  16. 16.
    Wald, L. (2002). Data fusion–Definitions and architectures—Fusion of images of different spatial resolutions. Paris: École de Mines de Paris.Google Scholar
  17. 17.
    Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., & Nencini, F. (2006). A new method for MS + pan image fusion assessment without reference. In: 2006 IEEE international geoscience and remote sensing symposium, (pp. 3802–3805). IGARSS, Denver, CO, USA, 31 July–4 August 2006, IEEE, 345 and 47th ST, New York, NY 10017 USA. ISBN: 078039510.Google Scholar
  18. 18.
    Damera-Venkata, N., Kite, T. D., & Geisler, W. S. (2000). Image quality assessment based on a degradation model. IEEE Transactions on Image Processing, 9(4), 636–650.CrossRefGoogle Scholar
  19. 19.
    Alparone, L., Wald, L., Chanussot, J., Thomas, C., Gamba, P., & Bruce, L. M. (2007). Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S Data-Fusion contest. IEEE Transactions on Geoscience and Remote Sensing, 10, 3012–3021. ISSN: 0196-2892.CrossRefGoogle Scholar

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© Korean Spatial Information Society 2018

Authors and Affiliations

  1. 1.SLRAD, National Remote Sensing CenterIndian Space Research OrganizationHyderabadIndia
  2. 2.Maulana Azad National Institute of TechnologyBhopalIndia

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