An Efficient Image Fusion Technique Using Wavelet with PCA

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 216)

Abstract

Image fusion is a process in which high-resolution Panchromatic Image (PAN) is combined with a low resolution Multispectral Image (MS) to form a new single image which contains both the spatial information of the PAN image and the spectral information of the MS image. By applying wavelet transform alone, the fusion result is often not good. Hence, when a wavelet transform is integrated with any traditional fusion method the fusion results are better. The decimated and undecimated wavelets used in image fusion can be categorized into three classes: Orthogonal, Biorthogonal, and Nonorthogonal. In this study, a fusion technique is proposed which uses both wavelet and PCA method for fusing the IRS-1D images using LISS III scanner for the locations Vishakhapatnam and Hyderabad, India. The proposed fusion results are compared using statistical performance measures and analyzed. It was ascertained that the wavelet with PCA is superior to the other wavelet transform methods.

Keywords

Image fusion Wavelet transforms Principal component analysis (PCA) Intensity-hue-saturation (IHS) Performance measures 

References

  1. 1.
  2. 2.
    Li, Z., Jing, Z., Yang, X., Sun, S.: Color transfer based remote sensing image fusion using non-separable wavelet frame transform. Pattern Recogn. Lett. 26 (13), 2006–2014 (2005)Google Scholar
  3. 3.
    Hong, G., Zhang, Y.: Comparison and improvement of wavelet-based image fusion. Int. J. Remote Sens. 29(3), 673–691 (2008)CrossRefGoogle Scholar
  4. 4.
    Chavez, P.S., Kwarteng, A.Y.: Extracting spectral contrast in land sat thematic mapper image data using selective principal component analysis. Photogramm. Eng. Remote Sens. 55, 339–348 (1989)Google Scholar
  5. 5.
    Pohl, C., Van Genderen, J.L.: Multisensor image fusion in remote sensing: concepts, methods and applications. Int. J. Remote Sens. 19, 823–854 (1998)CrossRefGoogle Scholar
  6. 6.
    Smith, A.R.: Color gamut transform pairs. Comput. Graph. 12, 12–19 (1978)CrossRefGoogle Scholar
  7. 7.
    ACM.: Status report of the graphics standard planning committee. Comput. Graph. 13 (3), 1979Google Scholar
  8. 8.
    Schetselaar, E.M.: Fusion by the IHS transform: should we use cylindrical or spherical coordinates? Int. J. Remote Sens. 19, 759–765 (1998)CrossRefGoogle Scholar
  9. 9.
    Chibani, Y., Houacine, A.: Fusion of multispectral and radar image in the redundant wavelet domain. In: Proceedings of the SPIE, vol. 3500, pp. 330–338, Sept. 1998Google Scholar
  10. 10.
    Gonzalez-Ausicana, M., Saleta, JL., Catalan, RG., Garcia, R.: Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition. IEEE Trans. Geosci. Remote Sens. 42(6), 2004Google Scholar
  11. 11.
    Shi, W., Zhu, C., Tian, Y., Nichol, J.: Wavelet-based image fusion and quality assessment. Int. J. Appl. Earth Obs. Geoinf. 6, 241–251 (2005)Google Scholar
  12. 12.
    Li, S., Li, Z., Gong, J.: Multivariate statistical of measures for assessing the quality of image fusion. Int. J. Image Data Fusion (2010)Google Scholar
  13. 13.
    Yuhendra, Wavelet PCA based images fusion techniques and quality assessmentGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer ApplicationsNalla Malla Reddy Engineering CollegeHyderabadIndia

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