TY - GEN
T1 - Unsupervised segmentation of hyperspectral images based on dominant edges
AU - Lee, Sangwook
AU - Lee, Sanghun
AU - Lee, Chulhee
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84906914280&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:84906914280
SN - 9789897580031
T3 - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
SP - 588
EP - 592
BT - VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications
PB - SciTePress
T2 - 9th International Conference on Computer Vision Theory and Applications, VISAPP 2014
Y2 - 5 January 2014 through 8 January 2014
ER -