Effective reconstructed methods of the CL multiwavelet for remote sensing images

Abstract

This article present a variety of newly reconstructed methods using the Chui–Lian (CL) multiwavelet for remote sensing images as well as evaluating the criteria for the reconstructed quality of the images. The relationships between the CL multiwavelet transformed coefficients resulting from both the same levels and different levels are derived. Two approaches are presented to reconstruct the compressed images: the non-predictive method and the self-adaptive expansion coefficient method. As the classical criteria used to evaluate the reconstructed image quality, that is the mean square error (MSE) and peak signal-to-noise ratio (PSNR), are no longer sufficient to evaluate the reconstructed quality of remote sensing images with the CL multiwavelet, two new criteria, the maximum square error of the pixel (MASEP) and the minimum square error of the pixel (MISEP), between the original image and the decoded image, respectively, are presented. Our methods and the new criteria are illustrated by an experiment with Satellite Pour l'Observation de la Terre (SPOT) image data.

Acknowledgements

I would like to thank Prof. Arthur Cracknell, as well as two referees, for useful comments on an earlier draft of this article.