Multispectral and Hyperspectral Image Fusion Based on Group Spectral Embedding and Low-Rank Factorization

Abstract

Fusing low spatial resolution hyperspectral (LRHS) images and high spatial resolution multispectral (HRMS) images to obtain high spatial resolution hyperspectral images (HRHS) has received increasing interests in recent years. In this paper, a new group spectral embedding (GSE)-based LRHS and HRMS image fusion method is proposed by exploring the multiple manifold structures of spectral bands and the low-rank structure of HRHS data. First, a low-rank factorization fusion (LRFF)-based robust recovery model is developed for HRHS images, by regarding HRMS images as the spectral degradation of HRHS images and exploring the group sparse prior of difference images. Then, an assumption that grouped spectral bands share the similar local geometry is cast on LRHS and HRHS images, to formulate a GSE regularizer in the LRFF model. Finally, an iterative optimization algorithm based on augmented Lagrangian multiplier is advanced to recover HRHS images. Experimental results on several data sets show the effectiveness of the proposed method on visual and numerical comparison.