Variational Fusion of Hyperspectral Data by Non-Local Filtering

Jamila Mifdal, Bartomeu Coll, Jacques Froment and Joan Duran
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Jamila Mifdal: Φ-Lab, European Space Agency, ESRIN, 00044 Frascati, Italy
Bartomeu Coll: Department of Mathematics and Computer Science and IAC3, Universitat de les Illes Balears, Cra. de Valldemossa km. 7.5, E-07122 Palma, Spain
Jacques Froment: Univ Bretagne-Sud, CNRS UMR 6205 LMBA, Campus de Tohannic, F-56000 Vannes, France
Joan Duran: Department of Mathematics and Computer Science and IAC3, Universitat de les Illes Balears, Cra. de Valldemossa km. 7.5, E-07122 Palma, Spain

Mathematics, 2021, vol. 9, issue 11, 1-22

Abstract: The fusion of multisensor data has attracted a lot of attention in computer vision, particularly among the remote sensing community. Hyperspectral image fusion consists in merging the spectral information of a hyperspectral image with the geometry of a multispectral one in order to infer an image with high spatial and spectral resolutions. In this paper, we propose a variational fusion model with a nonlocal regularization term that encodes patch-based filtering conditioned to the geometry of the multispectral data. We further incorporate a radiometric constraint that injects the high frequencies of the scene into the fused product with a band per band modulation according to the energy levels of the multispectral and hyperspectral images. The proposed approach proved robust to noise and aliasing. The experimental results demonstrate the performance of our method with respect to the state-of-the-art techniques on data acquired by commercial hyperspectral cameras and Earth observation satellites.

Keywords: data fusion; hyperspectral imaging; multispectral imaging; super-resolution; variational methods; non-local filtering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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