Two-dimensional fast Haar wavelet transform for satellite-image fusion

Ruben Javier Medina Daza; Carlos P. Ruiz; Luis Joyanes Aguilar

doi:10.1117/1.JRS.7.073698

24 September 2013 Two-dimensional fast Haar wavelet transform for satellite-image fusion

Ruben Javier Medina Daza, Carlos P. Ruiz, Luis Joyanes Aguilar

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073698 (September 2013). https://doi.org/10.1117/1.JRS.7.073698

Abstract

This work presents an implementation of the fast Haar wavelet transform (FHWT) mathematical algorithm assisted by the Mallat algorithm together with the ARSIS concept so as to successfully attain satellite image merging with different resolutions. Four pairs of images, namely (multispectral—pancromatic): IKONOS, GeoEye, OrbView-2, and Landsat ETM+, representing different environments are used to assess the present implementation of FHWT. In order to compare the performance of FHWT with other orthogonal and bi-orthogonal wavelets, the same satellite images are merged using other five wavelets from some of the MATLAB’s available sets, namely bior6.8, rbio6.8, db7, dmey, and Haar. After applying the six wavelets to the four regions under study, four indices are used to assess spatial and spectral quality of the merged images, namely correlation coefficient, relative average spectral error, relative dimensionless global error in synthesis, and the universal quality index Q. Moreover, shape recognition capacity is also assessed based on the resulting merged images. To do so, various objects are binarized in each of the images. Binarized versions of the objects are compared to the same objects obtained from their corresponding panchromatic image. These binarized versions are also assessed using kappa coefficient and overall accuracy. For each of the indices, the best results are obtained with the proposed method FHWT.

Citation Download Citation

Ruben Javier Medina Daza, Carlos P. Ruiz, and Luis Joyanes Aguilar "Two-dimensional fast Haar wavelet transform for satellite-image fusion," Journal of Applied Remote Sensing 7(1), 073698 (24 September 2013). https://doi.org/10.1117/1.JRS.7.073698

Published: 24 September 2013

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