Hyperspectral and Multispectral Image Fusion based on a Sparse Representation
This work addresses image fusion for remote sensing applications, but it is incremental as it builds on existing variational and sparse representation techniques.
The paper tackled the problem of fusing hyperspectral and multispectral images by formulating it as an inverse problem with sparse regularization, and the proposed algorithm demonstrated efficiency compared to state-of-the-art methods.
This paper presents a variational based approach to fusing hyperspectral and multispectral images. The fusion process is formulated as an inverse problem whose solution is the target image assumed to live in a much lower dimensional subspace. A sparse regularization term is carefully designed, relying on a decomposition of the scene on a set of dictionaries. The dictionary atoms and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved via alternating optimization with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed algorithm when compared with the state-of-the-art fusion methods.