Research Article Open Access

Application of Morphological Component Analysis to Optical Image Fusion

Georges Laussane Loum1, Atiampo Kodjo Armand1, Pandry Koffi Ghislain1 and Souleymane Oumtanaga1
  • 1 Institut National Polytechnique Félix Houphouet-Boigny, Cote D'Ivoire
American Journal of Applied Sciences
Volume 14 No. 8, 2017, 795-807

DOI: https://doi.org/10.3844/ajassp.2017.795.807

Submitted On: 13 February 2017 Published On: 11 August 2017

How to Cite: Loum, G. L., Armand, A. K., Ghislain, P. K. & Oumtanaga, S. (2017). Application of Morphological Component Analysis to Optical Image Fusion. American Journal of Applied Sciences, 14(8), 795-807. https://doi.org/10.3844/ajassp.2017.795.807

Abstract

The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information com pare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%. 

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Keywords

  • Image Pansharpening
  • Laplacian Pyramid
  • Bivariate Hahn polynomials
  • Texture Analysis