Remote sensing image fusion using Statistical Univariate Finite Mixture Model in Shearlet Domain

Abstract

Remote sensing image fusion is a process that integrates the spatial detail of panchromatic (PAN) image and the spectral information of a low-resolution multispectral (MS) image and produces a fused image that contain both high spatial and spectral details. In this paper, a new remote sensing image fusion method is proposed based on Statistical Univariate Finite Mixture Model (UFMM) in Shearlet Domain. Foremost, the Shearlet sub-bands for PAN and MS image are achieved by Shearlet Transform (ST). Latter, a novel fusion strategy is designed for both low and high pass sub-bands. Finally, the fused image is achieved by Inverse Shearlet Transform (IST). By comparing with the well-known methods in terms of several quality evaluation indexes, the experimental results on QuickBird and IKONOS images show the superiority of our method.