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Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification

DSpace at IIT Bombay

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Title Integration of IRS-1A L2 data by fuzzy logic approaches for landuse classification
 
Creator MOHAN, BK
MADHAVAN, BB
GUPTA, UMD
 
Subject image segmentation
region
edge
 
Description A methodology has been formulated to integrate images from IRS-1A LISS II of two dates for landuse/landcover classification. The methodology developed includes image classification by fuzzy k-means clustering and fusion of memberships by fuzzy set theoretic operators. The two date images have been geometrically coregistered and classified for the identification of land classes individually. The fuzzy memberships of the classified output images have been integrated by using fuzzy logic operators like algebraic sum and gamma (gamma) operator. The classification accuracy of the resultant land classes in the integrated images was verified with the ground data collected in situ. The resultant images have been evaluated by kappa (kappa) statistic and it was found that output from the image of fuzzy algebraic sum operator scored high in generating the land classes, with an overall accuracy of 95%.
 
Publisher TAYLOR & FRANCIS LTD
 
Date 2011-08-31T00:40:09Z
2011-12-26T12:59:15Z
2011-12-27T05:50:20Z
2011-08-31T00:40:09Z
2011-12-26T12:59:15Z
2011-12-27T05:50:20Z
2000
 
Type Article
 
Identifier INTERNATIONAL JOURNAL OF REMOTE SENSING, 21(8), 1709-1723
0143-1161
http://dx.doi.org/10.1080/014311600209986
http://dspace.library.iitb.ac.in/xmlui/handle/10054/12519
http://hdl.handle.net/10054/12519
 
Language en