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Remote Sens. 2017, 9(7), 662; doi:10.3390/rs9070662

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

1
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
2
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
3
Center for Research in Computer Vision, University of Central Florida, Orlando, FL 32816, USA
*
Author to whom correspondence should be addressed.
Received: 10 May 2017 / Revised: 19 June 2017 / Accepted: 25 June 2017 / Published: 28 June 2017
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
View Full-Text   |   Download PDF [2179 KB, uploaded 28 June 2017]   |  

Abstract

This paper investigates representation-based classification for multispectral imagery. Due to small spectral dimension, the performance of classification may be limited, and, in general, it is difficult to discriminate different classes with multispectral imagery. Nonlinear band generation method with explicit functions is proposed to use which can provide additional spectral information for multispectral image classification. Specifically, we propose the simple band ratio function, which can yield better performance than the nonlinear kernel method with implicit mapping function. Two representation-based classifiers—i.e., sparse representation classifier (SRC) and nearest regularized subspace (NRS) method—are evaluated on the nonlinearly generated datasets. Experimental results demonstrate that this dimensionality-expansion approach can outperform the traditional kernel method in terms of high classification accuracy and low computational cost when classifying multispectral imagery. View Full-Text
Keywords: multispectral imagery; nonlinear classification; kernel method; dimensionality expansion multispectral imagery; nonlinear classification; kernel method; dimensionality expansion
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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).

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Xu, Y.; Du, Q.; Li, W.; Chen, C.; Younan, N.H. Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers. Remote Sens. 2017, 9, 662.

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