Next Article in Journal
A Survey of Online Activity Recognition Using Mobile Phones
Previous Article in Journal
Double Cluster Heads Model for Secure and Accurate Data Fusion in Wireless Sensor Networks
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(1), 2041-2058; doi:10.3390/s150102041

Hyperspectral Imagery Super-Resolution by Compressive Sensing Inspired Dictionary Learning and Spatial-Spectral Regularization

1
School of Computer Science and Engineering, Nanjing University of Science & Technology, Nanjing 210094, China
2
School of Science, Nanjing University of Science & Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Received: 26 August 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [1873 KB, uploaded 19 January 2015]   |  

Abstract

Due to the instrumental and imaging optics limitations, it is difficult to acquire high spatial resolution hyperspectral imagery (HSI). Super-resolution (SR) imagery aims at inferring high quality images of a given scene from degraded versions of the same scene. This paper proposes a novel hyperspectral imagery super-resolution (HSI-SR) method via dictionary learning and spatial-spectral regularization. The main contributions of this paper are twofold. First, inspired by the compressive sensing (CS) framework, for learning the high resolution dictionary, we encourage stronger sparsity on image patches and promote smaller coherence between the learned dictionary and sensing matrix. Thus, a sparsity and incoherence restricted dictionary learning method is proposed to achieve higher efficiency sparse representation. Second, a variational regularization model combing a spatial sparsity regularization term and a new local spectral similarity preserving term is proposed to integrate the spectral and spatial-contextual information of the HSI. Experimental results show that the proposed method can effectively recover spatial information and better preserve spectral information. The high spatial resolution HSI reconstructed by the proposed method outperforms reconstructed results by other well-known methods in terms of both objective measurements and visual evaluation. View Full-Text
Keywords: compressive sensing; dictionary learning; super-resolution; hyperspectral image; spectral similarity; sparse representation compressive sensing; dictionary learning; super-resolution; hyperspectral image; spectral similarity; sparse representation
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

Huang, W.; Xiao, L.; Liu, H.; Wei, Z. Hyperspectral Imagery Super-Resolution by Compressive Sensing Inspired Dictionary Learning and Spatial-Spectral Regularization. Sensors 2015, 15, 2041-2058.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top