计算机科学 ›› 2019, Vol. 46 ›› Issue (5): 235-240.doi: 10.11896/j.issn.1002-137X.2019.05.036
田振坤1,2, 傅莺莺3, 刘素红4,5
TIAN Zhen-kun1,2, FU Ying-ying3, LIU Su-hong4,5
摘要: 针对信息获取与处理过程中的不确定性导致的遥感数据分类精度难以满足土地覆盖变化、环境监测、专题信息提取等应用方面的需求,提出了一种基于机器学习的分类融合算法。采用6种异构分类器,以查准率及查全率矩阵为先验知识,依据分类器差异性指数AD对单分类器进行优化组合,结合三维概率矩阵分别得到抽象级、排序级和度量级的分类融合结果输出,并以北京地区Landsat 8遥感影像的典型区域为研究对象进行分类预测。结果表明,从6个单分类器中选取3个进行组合时的效果较好,其中AD值最大的(NB,KNN,SVM)分类器组合是综合效果最好的分类器组合;所提算法的抽象级输出比单分类的平均精度高12.28%,比分类效果最好的单分类器SVM高2.24%;所提算法对多个“强成员分类器”进行融合仍然能有效提高分类精度,比常用融合算法RF,Bagging和Boosting分别高出11.23%,7.56%和11.36%,对各种地物的分类精度有显著的提高。
中图分类号:
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