Comparison of Nine Fusion Techniques for Very High Resolution Data

This study compares the efficiency of 9 image fusion techniques and more specifically the efficiency of IHS, Modified IHS, PCA, Pansharp, Wavelet, LMM (Local Mean Matching), LMVM (Local Mean and Variance Matching), Brovey, and Multiplicative fusion techniques for the fusion of QuickBird data. The suitability of these fusion techniques for various applications depends on the spectral and spatial quality of the fused images. In order to quantitatively measure the quality of the fused images, the author has made the following controls. First, the visual qualitative result is examined. Next, the correlation between the original multispectral and the fused images and all the statistical parameters of the histograms of the various frequency bands is studied. Lastly, an unsupervised classification is performed, and the resulting images are compared. All the fusion techniques improve the resolution and the visual result. The resampling method practically has no effect on the final visual result. The LMVM, the LMM, the Pansharp, and the Wavelet merging technique do not change the statistical parameters of the original images. The Modified IHS provokes minor changes to the statistical parameters as compared to the classical IHS or the PCA. After all the controls, the LMVM, the LMM, the Pansharp, and the Modified IHS algorithm seem to offer the most advantages in fusion panchromatic and multispectral data.

  • Availability:
  • Authors:
    • Nikolakopoulos, Konstantinos G
  • Publication Date: 2008-5

Language

  • English

Media Info

  • Media Type: Print
  • Features: Figures; References;
  • Pagination: pp 647-659
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01103048
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 24 2008 7:42AM