An optimal-truncation-based tucker decomposition method for hyperspectral image compression

Hao Chen; Wei Lei; Shuang Zhou; Ye Zhang

doi:10.1109/IGARSS.2012.6350833

An optimal-truncation-based tucker decomposition method for hyperspectral image compression

Hao Chen^*
, Wei Lei
, Shuang Zhou
, Ye Zhang

^*Corresponding author for this work

School of Electronics and Information Engineering, Harbin Institute of Technology

Research output: Contribution to conference › Paper › peer-review

2 Scopus citations

Abstract

Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.

Original language	English
Pages	4090-4093
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS.2012.6350833
State	Published - 2012
Externally published	Yes
Event	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012 - Munich, Germany Duration: 22 Jul 2012 → 27 Jul 2012

Conference

Conference	2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012
Country/Territory	Germany
City	Munich
Period	22/07/12 → 27/07/12

Keywords

Hyperspectral images
Image Compression
Optimal truncation
Tucker Decomposition

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10.1109/IGARSS.2012.6350833

Cite this

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title = "An optimal-truncation-based tucker decomposition method for hyperspectral image compression",

abstract = "Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.",

keywords = "Hyperspectral images, Image Compression, Optimal truncation, Tucker Decomposition",

author = "Hao Chen and Wei Lei and Shuang Zhou and Ye Zhang",

year = "2012",

doi = "10.1109/IGARSS.2012.6350833",

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AU - Chen, Hao

AU - Lei, Wei

AU - Zhou, Shuang

AU - Zhang, Ye

PY - 2012

Y1 - 2012

N2 - Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.

AB - Hyperspectral images (HSI) contain hundreds of bands, which brings huge amount of data. In this paper, a novel compression method based on optimal-truncation tucker decomposition for HSI is proposed. HSI tensor is firstly decomposed into complete core tensor. And then core tensor and factor matrices are truncated according to the optimal number of components of core tensor along each mode (NCCTEM), which is determined by the proposed criterion for the optimal NCCTEM and searching strategy. Experimental results show that the proposed method has the excellent reconstruction comparable to the traditional compression methods. Furthermore, it significantly reduces the compression and decompression time.

KW - Hyperspectral images

KW - Image Compression

KW - Optimal truncation

KW - Tucker Decomposition

UR - https://www.scopus.com/pages/publications/84873116430

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DO - 10.1109/IGARSS.2012.6350833

M3 - 论文

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T2 - 2012 32nd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012

Y2 - 22 July 2012 through 27 July 2012

ER -

An optimal-truncation-based tucker decomposition method for hyperspectral image compression

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Keywords

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