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Hyperspectral and Multispectral Image Fusion based on a Non-locally Centralized Sparse Model and Adaptive Spatial-Spectral Dictionaries

Hyperspectral (HS) imaging systems are useful in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of a HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is a HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem. The dictionaries are learned from the observed data taking advantage of the high spectral correlation within the HS image and the non-local self-similarity over the spatial domain of the MS image. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm. Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.