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《Science of Surveying and Mapping》 2009-01
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Algorithm research of maximum likelihood classification based on fuzzy inference

WANG Jing-xue①,SONG Wei-dong①,WANG Wei-xi①,GAO Feng②(① School of Geomatics,Liaoning Technique University,Fuxin 123000,China;② Liaoning Environment Monitoring Center,Shenyang 110031,China)  
This paper integrates the principle of Fuzzy Set with the principle of Maximum Likelihood Classification(MLC),and substitutes the traditional mean value and covariance matrix in MLC using the fuzzy mean value and fuzzy covariance.Then it improves the MLC algorithm based on the principle of maximum membershi Pdegree.Furthermore,this paper adopts one kind of classification precision evaluation methods based on the algorithm of space distribution of different classified pixels,and obtains the space distribution ma Pof different classified pixels.It also carries on the precision evaluation of classification based on this map.The experimental results indicate that the total classification precision and the Kappa coefficient of the improved MLC surpass the precision of traditional MLC method,and the new precision evaluation method is better than the traditional methods in the respects of validity,efficiency and so on.
【CateGory Index】: TP751
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