Performance Analysis of Contourlet-based Hyperspectral Image Fusion Methods

Recently, contourlet transform has been widely used in hyperspectral image fusion due to its advantages, such as high directionality and anisotropy; and studies show that the contourlet-based fusion methods perform better than the existing conventional methods including wavelet-based fusion methods. Few studies have been done to comparatively analyze the performance of contourlet-based fusion methods; furthermore, no research has been done to analyze the contourlet-based fusion methods by focusing on their unique transform mechanisms. In addition, no research has focused on the original contourlet transform and its upgraded versions. In this paper, we investigate three different kinds of contourlet transform: i) original contourlet transform, ii) nonsubsampled contourlet transform, iii) contourlet transform with sharp frequency localization. The latter two transforms were developed to overcome the major drawbacks of the original contourlet transform; so it is necessary and beneficial to see how they perform in the context of hyperspectral image fusion. The results of our comparative analysis show that the latter two transforms perform better than the original contourlet transform in terms of increasing spatial resolution and preserving spectral information.