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Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modified Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique

DSpace at IIT Bombay

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Title Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modified Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique
 
Creator BANERJEE, B
VARMA, S
BUDDHIRAJU, KM
EETI, LN
 
Subject Graph based clustering
image segmentation
mean-shift
minimum spanning tree
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ALGORITHM
 
Description An unsupervised object based segmentation, combining a modified mean-shift (MS) and a novel minimum spanning tree (MST) based clustering approach of remotely sensed satellite images has been proposed in this correspondence. The image is first pre-processed by a modified version of the standard MS based segmentation which preserves the desirable discontinuities present in the image and guarantees oversegmentation in the output. A nearest neighbor based method for estimating the bandwidth of the kernel density estimator (KDE) and a novel termination condition have been incorporated into the standard MS. Considering the segmented regions as nodes in a low level feature space, an MST is constructed. An unsupervised technique to cluster a given MST has also been devised here. This type of hybrid segmentation technique which clusters the regions instead of image pixels reduces greatly the sensitivity to noise and enhances the overall segmentation performance. The superiority of the proposed method has been experimented on a large set of multi-spectral images and compared with some well-known hybrid segmentation models.
 
Publisher IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
 
Date 2014-12-29T04:51:11Z
2014-12-29T04:51:11Z
2014
 
Type Article
 
Identifier IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 7(3)888-894
1939-1404
2151-1535
http://dx.doi.org/10.1109/JSTARS.2013.2266572
http://dspace.library.iitb.ac.in/jspui/handle/100/17121
 
Language English