A new pansharpening method based on the sparse representation of classified injected details over a featured dictionary

Abstract

ABSTRACT In this paper, the characteristics of low-resolution multispectral image (LRMS) and panchromatic image (PAN) fusion methods are investigated. Recently, the sparse representation (SR) based methods and the injected details (ID) based methods have been combined as the pansharpening method based on SR of ID. This novel method can gain better results than the former ones, but it also faces two disadvantages, i.e., the choice of using the raw patches as dictionary and using the SR to all parts of the aiming image are not so optimal, which will cause inaccurate representation and sharper than it should be in the smooth area. Thus, we propose a new pansharpening method based on SR of classified ID over featured dictionary to learn dictionary from the featured details and fuse different patches diversely to overcome the drawbacks mentioned above and enhance the quality of aiming image. The experimental results using QuickBird and WorldView2 show that the proposed method can achieve remarkable spectral and spatial quality.