Next Article in Journal
Lateral Offset Quality Rating along Low Slip Rate Faults: Application to the Alhama de Murcia Fault (SE Iberian Peninsula)
Previous Article in Journal
Hyperspectral Differentiation of Phytoplankton Taxonomic Groups: A Comparison between Using Remote Sensing Reflectance and Absorption Spectra
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(11), 14806-14826;

Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Intelligent Fusion Technology, Germantown, MD 20876, USA
Authors to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 17 June 2015 / Revised: 9 September 2015 / Accepted: 30 October 2015 / Published: 6 November 2015
Full-Text   |   PDF [1339 KB, uploaded 6 November 2015]   |  


Spatial texture features have been demonstrated to be very useful for the recently-proposed representation-based classifiers, such as the sparse representation-based classifier (SRC) and nearest regularized subspace (NRS). In this work, a weighted residual-fusion-based strategy with multiple features is proposed for these classifiers. Multiple features include local binary patterns (LBP), Gabor features, and the original spectral signatures. In the proposed classification framework, representation residuals for a testing pixel from using each type of features are weighted to generate the final representation residual, and then the label of the testing pixel is determined according to the class yielding the minimum final residual. The motivation of this work is that different features represent pixels from different perspectives and their fusion in the residual domain can enhance the discriminative ability. Experimental results of several real hyperspectral image datasets demonstrate that the proposed residual-based fusion outperforms the original NRS, SRC, support vector machine (SVM) with LBP, and SVM with Gabor features, even in small-sample-size (SSS) situations. View Full-Text
Keywords: local binary patterns (LBP); nearest regularized subspace (NRS); Gabor features; hyperspectral image classification local binary patterns (LBP); nearest regularized subspace (NRS); Gabor features; hyperspectral image classification

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

Peng, B.; Li, W.; Xie, X.; Du, Q.; Liu, K. Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery. Remote Sens. 2015, 7, 14806-14826.

Show more citation formats Show less citations formats

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