Adaptive signal representation and multi-scale decomposition for panchromatic and multispectral image fusion

Abstract

Abstract Multispectral images acquired by remote sensors although have a relative low spatial resolution, are valuable sources of spectral information. In contrast, panchromatic images with high spatial resolution have a lack of spectral features. Therefore, fusion of multispectral and panchromatic images called pansharpening provides an image that simultaneously has both of high spectral and spatial resolution. A hybrid pansharpening method that benefits the advantages of a multi-scale decomposition transform and free distribution model based methods is introduced in this work. The proposed method uses collaborative representation (CR) in addition to sparse representation (SR) for fusion of low pass components of multispectral and panchromatic images. An image consists of a background, which composes the majority of scene, and some targets, which have different spectral signatures with respect to background and occur with lower probability than it. In SR, just a few dictionary atoms contribute in signal representation while in CR all of dictionary atoms contribute for signal approximation. Therefore, SR is more appropriate for targets approximation while CR is more appropriate for background estimation. An anomaly score (AS) is computed for each image patch. Proportional to the calculated AS, a combination of SR and CR are adaptively used for approximation of image patch and generation of the fusion product. The proposed method is compared to some state-of-the-art pansharpening methods in terms of both the quality measures and visual analysis. The experiments are done on four datasets acquired by different sensors. The results show the superior performance of the proposed pansharpening method compared to different component substitution, multiresolution analysis, SR, and several hybrid methods.