%0 Journal Article
%T Study on Combined Classifier Based on Error Analysis<br>基于误差分析的组合分类器研究
%A 陈学泓
%A 陈晋
%A 杨伟
%A 朱锴
%J 遥感学报
%D 2008
%I 
%X Remote sensing iswidely used inmapping land use /land cover types and monitoring land use /land cover changes from regional to global scale. Supervised classificationmethod is a powerful tool in extracting land cover and land use information from remotely sensed mi ages. Although many supervised classification method have been developed in machine learning field, there are not a universal best performingmethod yet. That is, different kinds of classification methods have theirown advantages and defects. Thisphenomenon is called selective superiority. It isnecessary to explore amethod thatcan integrate advantagesofdifferentclassifiers and avoid theirweakness. Combining classifiersproperlymay mi prove classification accuracy, because different classifiersmay have differentmistake sets. Combined classifiers have been studiedwidely inmachine learning field; however, itwas seldom studied in remote sensing mi age classification. This paper proposed one type of combined classifier based on error analysis, which incorporates the rule outputs ofmaxmi um likelihood classification (MLC) and supportvectormachine (SVM), to achieve higher classification accuracy. MLC is themostwidely used classificationmethod in computer processing of remotely sensed mi ages. It is based on classical statistical theory and has solid probabilitymeanings. However, the classified accuracy of thismethod would be affected seriously if the training sample distribution does not follow normal distribution. SVM is a newly developed classifier, which is based on statistical learning theory. SVM is robust for small sample, and it has shown a good performance inmany studies. However, the originalSVM classifier is a binary classifier, which needs to be extended to a multi-class classifier through extraworks. How to effectively extend binary SVM tomulti-class classification is still an on- going research issue and itprobably affect the performance ofSVM. The newmethod proposed in thispaper firstestmi ates the errorsoftwo classifiers, which are denoted by the confidence intervalsofrule outputs, then combines their rule outputs withweights depending on the confidence intervals, and finally acquires a more accurate rule output. Classification expermi entswere conducted on case study area (Summer Palace area in Beijing). Classification accuracies of the combined classifier and two single classifiers were compared with different sample distribution and different sample amount. And the results demonstrated that the new combined classifier can acquire a higher accuracy than other two classifiers. The results also revealed that combined classifier performs betterwhen two classifiers aremore independent. Another compared expermi entwas done between new combined classifier and previous combined classifier by averaging, and resultalso showed thatnewmethod had betterperformance. However, there are still some defects in the newmethod. Firstly, error analysis is not completely finished for the two classif
%K combined classifier
%K error analysis
%K confidence interva
%K l land use/ land cover
%K remote sensing<br>组合分类器
%K 误差分析
%K 置信区间
%K 土地利用
%K 遥感
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=0ADB1C49173C51B801A73048B6EA7210&yid=67289AFF6305E306&vid=59906B3B2830C2C5&iid=94C357A881DFC066&journal_id=1007-4619&journal_name=遥感学报&referenced_num=0&reference_num=19