Hyperspectral Pansharpening via Multitask Joint Sparse Representation

Abstract

In this paper, a high spatial resolution (HR) hyperspectral image is inferred from a low spatial resolution (LR) hyperspectral image and a HR panchromatic image by taking advantage of the sparse representation pansharpening (SRP) method. Different from the conventional SRP or joint SRP (JSRP) method, this paper proposes a multitask JSRP method for hyperspectral pansharpening, in order to improve the generalization performance of the model. First, multiple HR/LR dictionary pairs are generated by partitioning the multiple features of the panchromatic image and their corresponding downsampled LR versions into patches. Second, the patch-level sparse representation coefficients of the multiple LR hyperspectral image features are jointly estimated under the multiple LR dictionaries. Finally, the estimated sparse representation coefficients are utilized to reconstruct the HR patches under the original HR dictionary, and the desired HR hyperspectral image is obtained by aggregating the HR patches. Experimental results conducted on two hyperspectral scenes demonstrate the effectiveness of the proposed method.