Image Fusion Based on PCA and Undecimated Discrete Wavelet Transform

  • Wei Liu
  • Jie Huang
  • Yongjun Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)


On the basis of analyzing the performances of popular image fusion methods, a new remote sensing image fusion method based on principal component analysis (PCA), high pass filter (HPF) and undecimated discrete wavelet transform (UDWT) is proposed. Some measure parameters are suggested to evaluate the fusion method. Experiments have been performed with the SPOT panchromatic image and the TM multi-spectral image. Both subjectively qualitative analysis and objectively quantitative evaluation verify the performance of the new method. With the same wavelet transform level, the fusion image using the proposed method preserves more sophisticated spatial details and distorts less spectral information in comparison with the fusion image using the traditional discrete wavelet transform (DWT) method.


Discrete Wavelet Transform Fusion Image Discrete Wavelet Spectral Information High Pass Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Shettigara, V.K.: A generalized component substitution technique for spatial enhancement of multispectral images using a higher resolution data set. Photogrammetric Engineering and Remote Sensing 58(5), 561–567 (1992)Google Scholar
  2. 2.
    Carper, W.J., Lillesand, T.M., Kiefer, R.W.: The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Enginering and Remote Sensing 56(4), 459–467 (1990)Google Scholar
  3. 3.
    Chavez, P.S., Sides, S.C., Anderson, J.A.: Comparison of three different methods to merge multi-resolution and multi-spectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing 57(3), 295–303 (1991)Google Scholar
  4. 4.
    Li, H., Manjunath, B.S., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graphical Models and Image Processing 27(3), 235–244 (1995)zbMATHCrossRefGoogle Scholar
  5. 5.
    Nunez, J., Otazu, X., Fors, O., Prades, A., Pala, V., Arbiol, R.: Multiresolution-Based Image Fusion with Additive Wavelet Decomposition. IEEE Trans. on Geosciences and Remote Sensing 37(3), 1204–1211 (1999)CrossRefGoogle Scholar
  6. 6.
    Bruno, A., Luciano, A., Stefano, B., Andrea, G.: Context-Driven Fusion of High Spatial and Spectral Resolution Images Based on Oversampled Multiresolution Analysis. IEEE Trans. on Geosciences and Remote Sensing 40(10), 2300–2312 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wei Liu
    • 1
  • Jie Huang
    • 1
  • Yongjun Zhao
    • 1
  1. 1.Information Science and Technology InstituteHenanChina

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