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Remote Sens. 2017, 9(6), 541; doi:10.3390/rs9060541

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

1
Department of Electrical and Electronics Engineering, Shiraz University of Technology, 13876-71557 Shiraz, Iran
2
Department of Physics, Shahid Bahonar University of Kerman, 7616914111 Kerman, Iran
3
Vision Lab, University of Antwerp, 2610 Antwerp, Belgium
*
Authors to whom correspondence should be addressed.
Academic Editors: Jonathan Cheung-Wai Chan, Yongqiang Zhao, Naoto Yokoya and Prasad S. Thenkabail
Received: 9 February 2017 / Revised: 17 May 2017 / Accepted: 23 May 2017 / Published: 31 May 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
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Abstract

In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently. View Full-Text
Keywords: hyperspectral image; multispectral image; Bayesian sparse representation; spectral unmixing hyperspectral image; multispectral image; Bayesian sparse representation; spectral unmixing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ghasrodashti, E.K.; Karami, A.; Heylen, R.; Scheunders, P. Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation. Remote Sens. 2017, 9, 541.

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