Automated display of hyperspectral images with unsupervised segmentation

Sangwook Lee; Jonghwa Lee; Chulhee Lee

doi:10.1117/12.826962

Automated display of hyperspectral images with unsupervised segmentation

Sangwook Lee, Jonghwa Lee, Chulhee Lee

Department of Electrical and Electronic Engineering

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

Abstract

In this paper, we investigate automated display methods for hyperspectral images with unsupervised segmentation. First, we apply an unsupervised segmentation method, which will produce a number of unlabeled classes. Then, we choose the classes whose sizes are larger than a threshold value. Then, we apply a feature extraction method to the chosen classes and find dominant discriminant features, which are used to display the hyperspectral images. We also exploit the use of the principal component analysis for the display of hyperspectral images. Experimental images show that the color images produced by the proposed methods show interesting characteristics compared to the conventional pseudo-color image.

Original language	English
Title of host publication	Satellite Data Compression, Communication, and Processing V
DOIs	https://doi.org/10.1117/12.826962
Publication status	Published - 2009
Event	Satellite Data Compression, Communication, and Processing V - San Diego, CA, United States Duration: 2009 Aug 4 → 2009 Aug 5

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	7455
ISSN (Print)	0277-786X

Other

Other	Satellite Data Compression, Communication, and Processing V
Country/Territory	United States
City	San Diego, CA
Period	09/8/4 → 09/8/5

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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10.1117/12.826962

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@inproceedings{d69cb28c503e4529ac56aad21061c96c,

title = "Automated display of hyperspectral images with unsupervised segmentation",

abstract = "In this paper, we investigate automated display methods for hyperspectral images with unsupervised segmentation. First, we apply an unsupervised segmentation method, which will produce a number of unlabeled classes. Then, we choose the classes whose sizes are larger than a threshold value. Then, we apply a feature extraction method to the chosen classes and find dominant discriminant features, which are used to display the hyperspectral images. We also exploit the use of the principal component analysis for the display of hyperspectral images. Experimental images show that the color images produced by the proposed methods show interesting characteristics compared to the conventional pseudo-color image.",

author = "Sangwook Lee and Jonghwa Lee and Chulhee Lee",

year = "2009",

doi = "10.1117/12.826962",

language = "English",

isbn = "9780819477453",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

booktitle = "Satellite Data Compression, Communication, and Processing V",

note = "Satellite Data Compression, Communication, and Processing V ; Conference date: 04-08-2009 Through 05-08-2009",

}

Lee, S, Lee, J & Lee, C 2009, Automated display of hyperspectral images with unsupervised segmentation. in Satellite Data Compression, Communication, and Processing V., 74550Y, Proceedings of SPIE - The International Society for Optical Engineering, vol. 7455, Satellite Data Compression, Communication, and Processing V, San Diego, CA, United States, 09/8/4. https://doi.org/10.1117/12.826962

TY - GEN

T1 - Automated display of hyperspectral images with unsupervised segmentation

AU - Lee, Sangwook

AU - Lee, Jonghwa

AU - Lee, Chulhee

PY - 2009

Y1 - 2009

N2 - In this paper, we investigate automated display methods for hyperspectral images with unsupervised segmentation. First, we apply an unsupervised segmentation method, which will produce a number of unlabeled classes. Then, we choose the classes whose sizes are larger than a threshold value. Then, we apply a feature extraction method to the chosen classes and find dominant discriminant features, which are used to display the hyperspectral images. We also exploit the use of the principal component analysis for the display of hyperspectral images. Experimental images show that the color images produced by the proposed methods show interesting characteristics compared to the conventional pseudo-color image.

AB - In this paper, we investigate automated display methods for hyperspectral images with unsupervised segmentation. First, we apply an unsupervised segmentation method, which will produce a number of unlabeled classes. Then, we choose the classes whose sizes are larger than a threshold value. Then, we apply a feature extraction method to the chosen classes and find dominant discriminant features, which are used to display the hyperspectral images. We also exploit the use of the principal component analysis for the display of hyperspectral images. Experimental images show that the color images produced by the proposed methods show interesting characteristics compared to the conventional pseudo-color image.

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UR - http://www.scopus.com/inward/citedby.url?scp=70350180085&partnerID=8YFLogxK

U2 - 10.1117/12.826962

DO - 10.1117/12.826962

M3 - Conference contribution

AN - SCOPUS:70350180085

SN - 9780819477453

T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Satellite Data Compression, Communication, and Processing V

T2 - Satellite Data Compression, Communication, and Processing V

Y2 - 4 August 2009 through 5 August 2009

ER -

Automated display of hyperspectral images with unsupervised segmentation

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