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《Science of Surveying and Mapping》 2010-05
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High resolution remote sensing image classificatoin based on Boosting and Bagging algorithms

CHEN Shao-jie①,PANG Yun-feng②(①School of Resources Engineering,Longyan University,Fujian Longyan 364012,China;②Department of Production,Longkou Mining Industry Group,Shandong Longkou 265700,China)  
As an advanced direction in pattern recognition,the applications of multiple classifier system or multiple classifier integration in remote sensing will further improve the reliability and accuracy of classification and reduce uncertainty in results.Boosting and Bagging,as two popular classifier ensemble algorithms based on sample manipulation,are used to high resolution remote sensing image classification in this paper.By using QuickBird image as the data souce and BPNN,RBFNN and decision tree as base classifiers,the performance of Boosting and Bagging to high resolution remote sensing image classification is analyzed.The results show that Boosting and Bagging are effective to high resolution remote sensing classification.
【CateGory Index】: TP751
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