A New Satellite Image Fusion Method Based on Distributed Compressed Sensing

Abstract

In this paper, we propose a method for fusion of low-resolution multispectral (LRM) image and high-resolution panchromatic (HRP) image to obtain high-resolution multispectral (HRM) image based on distributed compressed sensing (DCS). In the proposed method, HRP image is firstly used to obtain approximation and detail dictionary. Then, joint-sparsity-model-1 (JSM-1) is applied directly to both LRM bands and HRM bands. Each band in LRM image is decomposed into common component and innovation component which can be sparsely represented over the approximation dictionary. Based on Orthogonal Matching Pursuit (OMP) algorithm, the sparse coefficients are calculated from JSM-1 of the LRM image. Lastly, each band in HRM image is modeled as the fusion of the corresponding LRM band and detail band over the detail dictionary. Two datasets are used in the experiments to validate the proposed method and the results show that the proposed method has better performance than the traditional methods.