Hyperspectral and multispectral image fusion based on a sparse representation

Qi Wei (1) , José M. Bioucas-Dias (2) , Nicolas Dobigeon (1, 3) , Jean-Yves Tourneret (4, 3)

1 IRIT-SC - Signal et Communications
2 ULISBOA - Universidade de Lisboa = University of Lisbon
3 Toulouse INP - Institut National Polytechnique (Toulouse)
4 IRIT-MINDS - CoMputational imagINg anD viSion

This paper presents a variational-based approach for fusing hyperspectral and multispectral images. The fusion problem is formulated as an inverse problem whose solution is the target image assumed to live in a 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 supports of the corresponding 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 state-of-the-art fusion methods.

Mots clés: Image fusion; Hyperspectral (HS) image; Dictionary; Sparse representation; Multispectral (MS) image; Alternating direction method of multipliers (ADMM)

Domaines: Traitement des images [eess.IV]

Qi Wei, José M. Bioucas-Dias, Nicolas Dobigeon, Jean-Yves Tourneret. Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 2015, vol. 53 (n° 7), pp. 3658-3668. ⟨10.1109/TGRS.2014.2381272⟩. ⟨hal-01168121⟩