Nonlinear classification of multispectral imagery using representation-based classifiers

Yan Xu; Qian Du; Wei Li; Chen Chen; Nicolas H. Younan

doi:10.1109/IGARSS.2017.8128060

Nonlinear classification of multispectral imagery using representation-based classifiers

Yan Xu
, Qian Du
, Wei Li
, Chen Chen
, Nicolas H. Younan

Mississippi State University
Beijing University of Chemical Technology
University of Central Florida

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.

Original language	English
Title of host publication	2017 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	International Cooperation for Global Awareness, IGARSS 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4738-4741
Number of pages	4
ISBN (Electronic)	9781509049516
DOIs	https://doi.org/10.1109/IGARSS.2017.8128060
Publication status	Published - 1 Dec 2017
Externally published	Yes
Event	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States Duration: 23 Jul 2017 → 28 Jul 2017

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2017-July

Conference

Conference	37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Country/Territory	United States
City	Fort Worth
Period	23/07/17 → 28/07/17

Keywords

Kernel method
Multispectral imagery
Nonlinear classification

Access to Document

10.1109/IGARSS.2017.8128060

Cite this

Xu, Y., Du, Q., Li, W., Chen, C., & Younan, N. H. (2017). Nonlinear classification of multispectral imagery using representation-based classifiers. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings (pp. 4738-4741). Article 8128060 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2017.8128060

Xu, Yan ; Du, Qian ; Li, Wei et al. / Nonlinear classification of multispectral imagery using representation-based classifiers. 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4738-4741 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{23264ccb6bcd43f695cd1209eb62c2a6,

title = "Nonlinear classification of multispectral imagery using representation-based classifiers",

abstract = "The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.",

keywords = "Kernel method, Multispectral imagery, Nonlinear classification",

author = "Yan Xu and Qian Du and Wei Li and Chen Chen and Younan, \{Nicolas H.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 ; Conference date: 23-07-2017 Through 28-07-2017",

year = "2017",

month = dec,

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doi = "10.1109/IGARSS.2017.8128060",

language = "English",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Xu, Y, Du, Q, Li, W, Chen, C & Younan, NH 2017, Nonlinear classification of multispectral imagery using representation-based classifiers. in 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings., 8128060, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2017-July, Institute of Electrical and Electronics Engineers Inc., pp. 4738-4741, 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017, Fort Worth, United States, 23/07/17. https://doi.org/10.1109/IGARSS.2017.8128060

Nonlinear classification of multispectral imagery using representation-based classifiers. / Xu, Yan; Du, Qian; Li, Wei et al.
2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4738-4741 8128060 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Du, Qian

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AU - Chen, Chen

AU - Younan, Nicolas H.

PY - 2017/12/1

Y1 - 2017/12/1

N2 - The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.

AB - The paper investigates representation-based classification for multispectral imagery. Due to the limited spectral dimension, the performance may be limited, and, in general, it is difficult to discriminate different classes using multispectral imagery. Nonlinear band generation method is proposed to use which can provide additional spectral information for multispectral classification. Two classifiers, sparse representation-based classification (SRC) and Nearest Regularized Subspace (NRS) are evaluated on the generated datasets. The results show our approach can outperform other nonlinear method such as the traditional kernel method in terms of classification accuracy and computational cost.

KW - Kernel method

KW - Multispectral imagery

KW - Nonlinear classification

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DO - 10.1109/IGARSS.2017.8128060

M3 - Conference contribution

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BT - 2017 IEEE International Geoscience and Remote Sensing Symposium

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ER -

Xu Y, Du Q, Li W, Chen C, Younan NH. Nonlinear classification of multispectral imagery using representation-based classifiers. In 2017 IEEE International Geoscience and Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4738-4741. 8128060. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2017.8128060

Nonlinear classification of multispectral imagery using representation-based classifiers

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Keywords

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