Unsupervised Segmentation of Hyperspectral Images based on Dominant Edges

Sangwook Lee, Sanghun Lee, Chulhee Lee

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.

References

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Paper Citation


in Harvard Style

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 - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 588-592. DOI: 10.5220/0004739705880592


in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={588-592},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004739705880592},
isbn={978-989-758-003-1},
}


in EndNote Style

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