Sparse Representation Based Pansharpening Using Trained Dictionary

Abstract

Sparse representation has been used to fuse high-resolution panchromatic (HRP) and low-resolution multispectral (LRM) images. However, the approach faces the difficulty that the dictionary is generated from the high-resolution multispectral (HRM) images, which are unknown. In this letter, a two-step method is proposed to train the dictionary from the HRP and LRM images. In the first step, coarse HRM images are obtained by additive wavelet fusion method. The initial dictionary is composed of randomly sampled patches from the coarse HRM images. In the second step, a linear constraint K-SVD method is designed to train the dictionary to improve its representation ability. Experimental results using QuickBird and IKONOS data indicate that the trained dictionary yields comparable fusion products with raw-patch-dictionary sampled from HRM images.