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Open AccessArticle

Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints

1
Research and Development Institute, Northwestern Polytechnical University, Shenzhen 518057, China
2
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
3
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
4
Department of Computer Engineering, Sejong University, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 993; https://doi.org/10.3390/rs12060993
Received: 16 January 2020 / Revised: 14 February 2020 / Accepted: 10 March 2020 / Published: 19 March 2020
(This article belongs to the Special Issue Deep Learning and Feature Mining for Hyperspectral Imagery)
This paper presents a joint spatial-spectral resolution enhancement technique to improve the resolution of multispectral images in the spatial and spectral domain simultaneously. Reconstructed hyperspectral images (HSIs) from an input multispectral image represent the same scene in higher spatial resolution, with more spectral bands of narrower wavelength width than the input multispectral image. Many existing improvement techniques focus on spatial- or spectral-resolution enhancement, which may cause spectral distortions and spatial inconsistency. The proposed scheme introduces virtual intermediate variables to formulate a spectral observation model and a spatial observation model. The models alternately solve spectral dictionary and abundances to reconstruct desired high-resolution HSIs. An initial spectral dictionary is trained from prior HSIs captured in different landscapes. A spatial dictionary trained from a panchromatic image and its sparse coefficients provide high spatial-resolution information. The sparse coefficients are used as constraints to obtain high spatial-resolution abundances. Experiments performed on simulated datasets from AVIRIS/Landsat 7 and a real Hyperion/ALI dataset demonstrate that the proposed method outperforms the state-of-the-art spatial- and spectral-resolution enhancement methods. The proposed method also worked well for combination of exiting spatial- and spectral-resolution enhancement methods. View Full-Text
Keywords: hyperspectral images; joint spatial-spectral resolution enhancement; sparse representation hyperspectral images; joint spatial-spectral resolution enhancement; sparse representation
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Yi, C.; Zhao, Y.-Q.; Chan, J. .-W.; Kong, S.G. Joint Spatial-spectral Resolution Enhancement of Multispectral Images with Spectral Matrix Factorization and Spatial Sparsity Constraints. Remote Sens. 2020, 12, 993.

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