Next Article in Journal
Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)
Next Article in Special Issue
Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network
Previous Article in Journal
Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China
Previous Article in Special Issue
Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Article Menu
Issue 6 (June) cover image

Export Article

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

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

Department of Electrical and Electronics Engineering, Shiraz University of Technology, 13876-71557 Shiraz, Iran
Department of Physics, Shahid Bahonar University of Kerman, 7616914111 Kerman, Iran
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)
View Full-Text   |   Download PDF [1646 KB, uploaded 21 June 2017]   |  


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

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top