Unsupervised classification of remote sensing data using graph cut-based initialization
DSpace at IIT Bombay
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Title |
Unsupervised classification of remote sensing data using graph cut-based initialization
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Creator |
TYAGI, M
MEHRA, AK CHAUDHURI, S BRUZZONE, L |
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Description |
In this paper we propose a multistage unsupervised classifier which uses graph-cut to produce initial segments which are made up of pixels with similar spectral properties, subsequently labelled by a fuzzy c-means clustering algorithm into a known number of classes. These initial segmentation results are used as a seed to the expectation maximization (EM) algorithm. Final classification map is produced by using the maximum likelihood (ML) classifier, performance of which is quite good as compared to other unsupervised classification techniques.
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Publisher |
SPRINGER-VERLAG BERLIN
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Date |
2011-10-23T18:52:26Z
2011-12-15T09:11:17Z 2011-10-23T18:52:26Z 2011-12-15T09:11:17Z 2005 |
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Type |
Article; Proceedings Paper
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Identifier |
PATTERN RECOGNITION AND MACHINE INTELLIGENCE, PROCEEDINGS,3776,206-211
3-540-30506-8 0302-9743 http://dspace.library.iitb.ac.in/xmlui/handle/10054/15205 http://hdl.handle.net/100/1975 |
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Source |
1st International Conference on Pattern Recognition and Machine Intelligence,Kolkata, INDIA,DEC 20-22, 2005
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Language |
English
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