The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression

Hao Chen; Jiabin Wang; Shuang Zhou; Ye Zhang

doi:10.1109/DCC.2014.32

The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression

Hao Chen
, Jiabin Wang
, Shuang Zhou
, Ye Zhang

School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, we propose a novel compression method for HSI with Tucker3 decomposition. The hyperspectral images are firstly decomposed into core tensor, and then the number of components is selected according to the Fast particle swarm optimization (FPSO). Compared to the traditional methods, the new method has excellent reconstruction quality and less computing time.

Original language	English
Title of host publication	Proceedings - DCC 2014
Subtitle of host publication	2014 Data Compression Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	401
Number of pages	1
ISBN (Print)	9781479938827
DOIs	https://doi.org/10.1109/DCC.2014.32
State	Published - 2014
Externally published	Yes
Event	2014 Data Compression Conference, DCC 2014 - Snowbird, UT, United States Duration: 26 Mar 2014 → 28 Mar 2014

Publication series

Name	Data Compression Conference Proceedings
ISSN (Print)	1068-0314

Conference

Conference	2014 Data Compression Conference, DCC 2014
Country/Territory	United States
City	Snowbird, UT
Period	26/03/14 → 28/03/14

Keywords

Hyperspectral compression
Tensor

Access to Document

10.1109/DCC.2014.32

Cite this

Chen, H., Wang, J., Zhou, S., & Zhang, Y. (2014). The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression. In Proceedings - DCC 2014: 2014 Data Compression Conference (pp. 401). Article 6824453 (Data Compression Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC.2014.32

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title = "The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression",

abstract = "Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, we propose a novel compression method for HSI with Tucker3 decomposition. The hyperspectral images are firstly decomposed into core tensor, and then the number of components is selected according to the Fast particle swarm optimization (FPSO). Compared to the traditional methods, the new method has excellent reconstruction quality and less computing time.",

keywords = "Hyperspectral compression, Tensor",

author = "Hao Chen and Jiabin Wang and Shuang Zhou and Ye Zhang",

year = "2014",

doi = "10.1109/DCC.2014.32",

language = "英语",

isbn = "9781479938827",

series = "Data Compression Conference Proceedings",

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

pages = "401",

booktitle = "Proceedings - DCC 2014",

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note = "2014 Data Compression Conference, DCC 2014 ; Conference date: 26-03-2014 Through 28-03-2014",

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Chen, H, Wang, J, Zhou, S & Zhang, Y 2014, The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression. in Proceedings - DCC 2014: 2014 Data Compression Conference., 6824453, Data Compression Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 401, 2014 Data Compression Conference, DCC 2014, Snowbird, UT, United States, 26/03/14. https://doi.org/10.1109/DCC.2014.32

The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression. / Chen, Hao; Wang, Jiabin; Zhou, Shuang et al.
Proceedings - DCC 2014: 2014 Data Compression Conference. Institute of Electrical and Electronics Engineers Inc., 2014. p. 401 6824453 (Data Compression Conference Proceedings).

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

TY - GEN

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AU - Zhou, Shuang

AU - Zhang, Ye

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N2 - Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, we propose a novel compression method for HSI with Tucker3 decomposition. The hyperspectral images are firstly decomposed into core tensor, and then the number of components is selected according to the Fast particle swarm optimization (FPSO). Compared to the traditional methods, the new method has excellent reconstruction quality and less computing time.

AB - Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, we propose a novel compression method for HSI with Tucker3 decomposition. The hyperspectral images are firstly decomposed into core tensor, and then the number of components is selected according to the Fast particle swarm optimization (FPSO). Compared to the traditional methods, the new method has excellent reconstruction quality and less computing time.

KW - Hyperspectral compression

KW - Tensor

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DO - 10.1109/DCC.2014.32

M3 - 会议稿件

AN - SCOPUS:84903455055

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The FPSO for selecting number of components in Tucker3 decomposition for Hyperspectral image compression

Abstract

Publication series

Conference

Keywords

Access to Document

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