Spectral and spatial kernel extreme learning machine for hyperspectral image classification

Zhijing Yang; Faxian Cao; Jaime Zabalza; Weizhao Chen; Jiangzhong Cao

doi:10.1007/978-3-030-00563-4_38

Spectral and spatial kernel extreme learning machine for hyperspectral image classification

Zhijing Yang^*
, Faxian Cao
, Jaime Zabalza
, Weizhao Chen
, Jiangzhong Cao

^*Corresponding author for this work

Electronic And Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

2 Citations (Scopus)

Abstract

Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can't extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.

Original language	English
Title of host publication	Advances in Brain Inspired Cognitive Systems. BICS 2018
Editors	Amir Hussain, Bin Luo, Jiangbin Zheng, Xinbo Zhao, Cheng-Lin Liu, Jinchang Ren, Huimin Zhao
Place of Publication	Cham, Switzerland
Publisher	Springer-Verlag
Pages	394-401
Number of pages	8
ISBN (Print)	9783030005627
DOIs	https://doi.org/10.1007/978-3-030-00563-4_38
Publication status	Published - 6 Oct 2018
Event	9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 - Xi'an, China Duration: 7 Jul 2018 → 8 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10989
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018
Country/Territory	China
City	Xi'an
Period	7/07/18 → 8/07/18

Funding

Acknowledgements. This work is supported by the National Nature Science Foundation of China (nos. 61471132 and 61372173), and the Training program for outstanding young teachers in higher education institutions of Guangdong Province (no. YQ2015057).

Keywords

hyperspectral images (HSIs)
kernel extreme learning machine (KELM)
spectral and spatial information

Access to Document

10.1007/978-3-030-00563-4_38

Cite this

Yang, Z., Cao, F., Zabalza, J., Chen, W., & Cao, J. (2018). Spectral and spatial kernel extreme learning machine for hyperspectral image classification. In A. Hussain, B. Luo, J. Zheng, X. Zhao, C.-L. Liu, J. Ren, & H. Zhao (Eds.), Advances in Brain Inspired Cognitive Systems. BICS 2018 (pp. 394-401). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10989). Springer-Verlag. https://doi.org/10.1007/978-3-030-00563-4_38

Yang, Zhijing ; Cao, Faxian ; Zabalza, Jaime et al. / Spectral and spatial kernel extreme learning machine for hyperspectral image classification. Advances in Brain Inspired Cognitive Systems. BICS 2018. editor / Amir Hussain ; Bin Luo ; Jiangbin Zheng ; Xinbo Zhao ; Cheng-Lin Liu ; Jinchang Ren ; Huimin Zhao. Cham, Switzerland : Springer-Verlag, 2018. pp. 394-401 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9bd0af738ac84710b3380c566bcd5a56,

title = "Spectral and spatial kernel extreme learning machine for hyperspectral image classification",

abstract = "Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can't extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.",

keywords = "hyperspectral images (HSIs), kernel extreme learning machine (KELM), spectral and spatial information",

author = "Zhijing Yang and Faxian Cao and Jaime Zabalza and Weizhao Chen and Jiangzhong Cao",

year = "2018",

month = oct,

day = "6",

doi = "10.1007/978-3-030-00563-4\_38",

language = "English",

isbn = "9783030005627",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "394--401",

editor = "Amir Hussain and Bin Luo and Jiangbin Zheng and Xinbo Zhao and Cheng-Lin Liu and Jinchang Ren and Huimin Zhao",

booktitle = "Advances in Brain Inspired Cognitive Systems. BICS 2018",

note = "9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018 ; Conference date: 07-07-2018 Through 08-07-2018",

}

Yang, Z, Cao, F, Zabalza, J, Chen, W & Cao, J 2018, Spectral and spatial kernel extreme learning machine for hyperspectral image classification. in A Hussain, B Luo, J Zheng, X Zhao, C-L Liu, J Ren & H Zhao (eds), Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10989, Springer-Verlag, Cham, Switzerland, pp. 394-401, 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018, Xi'an, China, 7/07/18. https://doi.org/10.1007/978-3-030-00563-4_38

Spectral and spatial kernel extreme learning machine for hyperspectral image classification. / Yang, Zhijing; Cao, Faxian; Zabalza, Jaime et al.
Advances in Brain Inspired Cognitive Systems. BICS 2018. ed. / Amir Hussain; Bin Luo; Jiangbin Zheng; Xinbo Zhao; Cheng-Lin Liu; Jinchang Ren; Huimin Zhao. Cham, Switzerland: Springer-Verlag, 2018. p. 394-401 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10989).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Spectral and spatial kernel extreme learning machine for hyperspectral image classification

AU - Yang, Zhijing

AU - Cao, Faxian

AU - Zabalza, Jaime

AU - Chen, Weizhao

AU - Cao, Jiangzhong

PY - 2018/10/6

Y1 - 2018/10/6

N2 - Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can't extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.

AB - Kernel extreme learning machine (ELM) has attracted more and more attentions due to its good performance compared with support vector machine (SVM). Since the original Kernel ELM (KELM) is just a spectral classifier, it can't extract the rich spatial information of hyperspectral images (HSIs). This hence refrains the performance of KELM. In view of this, based on the fact that the neighbors of a pixel are more likely to belong to the same class, this paper proposes a spectral and spatial KELM, which exploits the local spatial information to improve the KELM for HSIs classification. Experimental results on two well-known datasets demonstrate the good performance of the proposed spectral and spatial KELM compared with the original KELM and other state-of-the-art methods.

KW - hyperspectral images (HSIs)

KW - kernel extreme learning machine (KELM)

KW - spectral and spatial information

UR - https://www.scopus.com/pages/publications/85055103303

U2 - 10.1007/978-3-030-00563-4_38

DO - 10.1007/978-3-030-00563-4_38

M3 - Conference contribution book

AN - SCOPUS:85055103303

SN - 9783030005627

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 394

EP - 401

BT - Advances in Brain Inspired Cognitive Systems. BICS 2018

A2 - Hussain, Amir

A2 - Luo, Bin

A2 - Zheng, Jiangbin

A2 - Zhao, Xinbo

A2 - Liu, Cheng-Lin

A2 - Ren, Jinchang

A2 - Zhao, Huimin

PB - Springer-Verlag

CY - Cham, Switzerland

T2 - 9th International Conference on Brain-Inspired Cognitive Systems, BICS 2018

Y2 - 7 July 2018 through 8 July 2018

ER -

Yang Z, Cao F, Zabalza J, Chen W, Cao J. Spectral and spatial kernel extreme learning machine for hyperspectral image classification. In Hussain A, Luo B, Zheng J, Zhao X, Liu CL, Ren J, Zhao H, editors, Advances in Brain Inspired Cognitive Systems. BICS 2018. Cham, Switzerland: Springer-Verlag. 2018. p. 394-401. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00563-4_38

Spectral and spatial kernel extreme learning machine for hyperspectral image classification

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