DocumentCode :
1431695
Title :
A Multispectral Image Segmentation Method Using Size-Weighted Fuzzy Clustering and Membership Connectedness
Author :
Hasanzadeh, M. ; Kasaei, S.
Author_Institution :
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
Volume :
7
Issue :
3
fYear :
2010
fDate :
7/1/2010 12:00:00 AM
Firstpage :
520
Lastpage :
524
Abstract :
Clustering-based image segmentation is a well-known multispectral image segmentation method. However, as it inherently does not account for the spatial relation among image pixels, it often results in inhomogeneous segmented regions. The recently proposed membership-connectedness (MC)-based segmentation method considers the local and global spatial relations besides the fuzzy clustering stage to improve segmentation accuracy. However, the inherent spatial and intraclass redundancies in multispectral images might decrease the accuracy and efficiency of the method. This letter addresses these two problems and proposes a segmentation method that is based on the MC method, watershed transform, and the proposed size-weighted fuzzy clustering method. The conducted experiments demonstrate the strength of the proposed algorithm in segmenting small objects, which plays an important role in remote-sensing image segmentation applications.
Keywords :
fuzzy set theory; image segmentation; pattern clustering; transforms; MC method; clustering-based image segmentation; global spatial relations; image pixels; inherent spatial redundancy; intraclass redundancy; local spatial relations; membership connectedness segmentation method; multispectral image segmentation method; remote sensing image segmentation; size-weighted fuzzy clustering method; watershed transform; Membership connectedness (MC); multispectral watershed transform; size-weighted fuzzy clustering;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing Letters, IEEE
Publisher :
ieee
ISSN :
1545-598X
Type :
jour
DOI :
10.1109/LGRS.2010.2040800
Filename :
5424062
Link To Document :
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