Transformwith Sharp Frequency Localization

This paper addresses four different aspects of the remote sensing image fusion: i) image fusion method, ii) quality analysis of fusion results, iii) effects of image decomposition level, and iv) importance of image registration. First, a new contourlet-based image fusion method is presented, which is an improvement over the wavelet-based fusion. This fusion method is then utilized withinthe main fusion process to analyze the final fusion results. Fusion framework, scheme and datasets used in the study are discussed in detail. Second, quality analysis of the fusion results is discussed using various quantitative metrics for both spatial and spectral analyses. Our results indicate that the proposed contourlet-based fusion method performs better than the conventional wavelet-based fusion methodsin terms of both spatial and spectral analyses. Third, we conducted an analysis on the effects of the image decomposition level and observed that the decomposition level of 3 produced better fusion results than both smaller and greater number of levels. Last, we created four different fusion scenarios to examine the importance of the image registration. As a result, the feature-based image registration using the edge features of the source images produced better fusion results than the intensity-based imageregistration.