Remote Sensing Image Fusion Using Ripplet Transform and Compressed Sensing

In this letter, we propose a novel remote sensing image fusion method based on the ripplet transform and the compressed sensing (CS) theory. The ripplet transform generalizes the curvelet transform by adding two parameters, namely, support c and degree d. These parameters provide the ripplet transform with anisotropy capability of representing singularities along arbitrarily shaped curves, and the curvelet transform is just a special case of the ripplet transform with c = 1 and d = 2. In the proposed method, the spatial details are first extracted from the PAN image by means of ripplets, and then, they are injected into the MS bands by the proposed injection model named CS-based injection. The aim of this model, which is based on the CS theory, is to minimize the spectral distortion in the pan-sharpened MS bands with respect to the original ones. The experimental results carried out on IKONOS and QuickBird data sets demonstrate that the proposed method provides better fused images in terms of the visual and quantitative evaluations.