An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion

Most earth observation satellites, such as IKONOS, QuickBird, GeoEye, and WorldView-2, provide a high spatial resolution (HR) panchromatic (Pan) image and a multispectral (MS) image at a lower spatial resolution (LR). Image fusion is an effective way to acquire the HR MS images that are widely used in various applications. In this paper, we propose an online coupled dictionary learning (OCDL) approach for image fusion, in which a superposition strategy is applied to construct the coupled dictionaries. The constructed coupled dictionaries are further developed via an iterative update to ensure that the HR MS image patch can be almost identically reconstructed by multiplying the HR dictionary and the sparse coefficient vector, which is solved by sparsely representing its counterpart LR MS image patch over the LR dictionary. The fusion results from IKONOS and WorldView-2 data show that the proposed fusion method is competitive or even superior to the other state-of-the-art fusion methods.