Next Article in Journal
Pointing Verification Method for Spaceborne Lidars
Next Article in Special Issue
No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning
Previous Article in Journal
Estimating Savanna Clumping Index Using Hemispherical Photographs Integrated with High Resolution Remote Sensing Images
Previous Article in Special Issue
Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(1), 53; doi:10.3390/rs9010053

Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation

1
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
2
Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
3
Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, SY23 3DB Aberystwyth, UK
*
Author to whom correspondence should be addressed.
Academic Editors: Yongqiang Zhao, Naoto Yokoya, Lenio Soares Galvao, Richard Gloaguen and Prasad S. Thenkabail
Received: 2 September 2016 / Revised: 28 December 2016 / Accepted: 3 January 2017 / Published: 9 January 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
View Full-Text   |   Download PDF [4917 KB, uploaded 9 January 2017]   |  

Abstract

Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local self-similarity of the hyperspectral image. The proposed approach fuses the hyperspectral image with a high-spatial-resolution multispectral image of the same scene to obtain a hyperspectral image with high spatial and spectral resolutions. The input hyperspectral image is used to train the spectral dictionary, while the sparse codes of the desired HSI are estimated by jointly encoding the similar pixels in each pixel group extracted from the high-spatial-resolution multispectral image. To improve the accuracy of the pixel group based non-local sparse representation, the similar pixels in a pixel group are selected by utilizing both the spectral and spatial information. The performance of the proposed approach is tested on two remote sensing image datasets. Experimental results suggest that the proposed method outperforms a number of sparse representation based fusion techniques, and can preserve the spectral information while recovering the spatial details under large magnification factors. View Full-Text
Keywords: spatial and spectral image fusion; spectral dictionary learning; spectral non-local self-similarity; pixel group based non-local sparse representation spatial and spectral image fusion; spectral dictionary learning; spectral non-local self-similarity; pixel group based non-local sparse representation
Figures

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

Yang, J.; Li, Y.; Chan, J.C.-W.; Shen, Q. Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation. Remote Sens. 2017, 9, 53.

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

1

Comments

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