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Pyramid-based multi-sensor image data fusion with enhancement of textural features

  • B. Aiazzi
  • L. Alparone
  • S. Baronti
  • V. Cappellini
  • R. Carlà
  • L. Mortelli
Poster Session A: Color & Texture, Enhancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

In this work, a multi-resolution procedure based on a generalized Laplacian pyramid (GLP) with a rational scale factor is proposed to merge image data of any resolution and represent them at any scale. The GLP-based data fusion is shown to be superior to those of a similar scheme based on the discrete wavelet transform (WT) according to a set of parameters established in the literature. The pyramid-generating filters can be easily designed for data of any resolutions, differently from the WT, whose filter-bank design is non-trivial when the ratio between the scales of the images to be merged is not a power of two. Remotely sensed images from Landsat TM and from Panchromatic SPOT are fused together. Textured regions are enhanced without losing their spectral signatures, thereby expediting automatic analyses for contextual interpretation of the environment.

Keywords

Thematic Mapper Wavelet Transform Data Fusion Landsat Thematic Mapper Laplacian Pyramid 
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.

References

  1. 1.
    R. C. Luo and M. G. Kay, “Multisensor integration and fusion in intelligent systems,” IEEE Trans. Systems, Man, and Cybernetics, 19(5), 901–931 (1989).Google Scholar
  2. 2.
    H. Li, B. S. Manjunath, and S. K. Mitra, “Multisensor image fusion using the wavelet transform,” CVGIP: Graphical Models and Image Processing, 57(3), 235–245 (1995).Google Scholar
  3. 3.
    P. S. Chavez Jr., S. C. Sides, and J. A. Anderson, “Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic,” Photogram. Engin. Remote Sensing, 57(3), 295–303 (1991).Google Scholar
  4. 4.
    S. Mallat, “A Theory for Multiresolution Signal Decomposition: the Wavelet Representation,” IEEE Trans. Pattern Anal. Machine Intell., 11(7), 674–693 (1989).Google Scholar
  5. 5.
    P. J. Burt, “The pyramid as a structure for efficient computation,” in Multiresolution Image Processing and Analysis, A. Rosenfeld (Ed.), Berlin, Springer-Verlag (1984).Google Scholar
  6. 6.
    M. Unser and A. Aldroubi, “Polynomial Splines and Wavelets-A Signal Processing Perspective”, in Wavelets-A Tutorial in Theory and Applications, C. K. Chui (Ed.), Academic Press, 91–122 (1992).Google Scholar
  7. 7.
    M. G. Kim, I. Dinstein, and L. Shaw, “A Prototype Filter Design Approach to Pyramid Generation,” IEEE Trans. Pattern Anal. Machine Intell., 15(12), 1233–1240 (1993).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • B. Aiazzi
    • 1
  • L. Alparone
    • 2
  • S. Baronti
    • 1
  • V. Cappellini
    • 2
  • R. Carlà
    • 1
  • L. Mortelli
    • 2
  1. 1.IROE “Nello Carrara” - CNRFirenzeItaly
  2. 2.Dip. Ing. ElettronicaUniversity of FlorenceFirenzeItaly

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