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Authors: Sangwook Lee ; Sanghun Lee and Chulhee Lee

Affiliation: Yonsei University, Korea, Republic of

Keyword(s): Segmentation, Hyperspectral Images, PCA, Dominant Edges.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Segmentation and Grouping

Abstract: In this paper, we propose a new unsupervised segmentation method for hyperspectral images based on dominant edge information. In the proposed algorithm, we first apply the principal component analysis and select the dominant eigenimages. Then edge operators and the histogram equalizer are applied to the selected eigenimages, which produces edge images. By combining these edge images, we obtain a binary edge image. Morphological operations are then applied to these binary edge image to remove erroneous edges. Experimental results show that the proposed algorithm produced satisfactory results without any user input.

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Paper citation in several formats:
Lee, S.; Lee, S. and Lee, C. (2014). Unsupervised Segmentation of Hyperspectral Images based on Dominant Edges. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP; ISBN 978-989-758-003-1; ISSN 2184-4321, SciTePress, pages 588-592. DOI: 10.5220/0004739705880592

@conference{visapp14,
author={Sangwook Lee. and Sanghun Lee. and Chulhee Lee.},
title={Unsupervised Segmentation of Hyperspectral Images based on Dominant Edges},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP},
year={2014},
pages={588-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004739705880592},
isbn={978-989-758-003-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 2: VISAPP
TI - Unsupervised Segmentation of Hyperspectral Images based on Dominant Edges
SN - 978-989-758-003-1
IS - 2184-4321
AU - Lee, S.
AU - Lee, S.
AU - Lee, C.
PY - 2014
SP - 588
EP - 592
DO - 10.5220/0004739705880592
PB - SciTePress