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Article

Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery

by 1, 1,*, 1,*, 2 and 3
1
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
2
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
3
Intelligent Fusion Technology, Germantown, MD 20876, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Remote Sens. 2015, 7(11), 14806-14826; https://doi.org/10.3390/rs71114806
Received: 17 June 2015 / Revised: 9 September 2015 / Accepted: 30 October 2015 / Published: 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
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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. https://doi.org/10.3390/rs71114806

AMA Style

Peng B, Li W, Xie X, Du Q, Liu K. Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery. Remote Sensing. 2015; 7(11):14806-14826. https://doi.org/10.3390/rs71114806

Chicago/Turabian Style

Peng, Bing, Wei Li, Xiaoming Xie, Qian Du, and Kui Liu. 2015. "Weighted-Fusion-Based Representation Classifiers for Hyperspectral Imagery" Remote Sensing 7, no. 11: 14806-14826. https://doi.org/10.3390/rs71114806

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