CAJ | 학술논문

由于高光谱数据具有波段多，数据量大等特点，对其进行降维处理成为高光谱遥感研究的一个重要问题。提出一种基于多分类器组合的高光谱波段选择方法，该方法通过遗传算法良好的寻优能力获得若干组较优初始波段子集，在此基础上使用这些波段子集训练若干个基分类器，进而利用改进的基于相同错误差异性度量的分类器选择方法选出部分较优分类器，实现波段选择的目的；最终通过局部精度分析的动态分类器选择实现多分类器组合决策。在公共测试数据集上的实验结果表明：与以往直接选择最优波段子集方法相比，提出的算法能够选择更多具有鉴别能力的波段，明显提高了分类正确率。
유우고광보수거구유파단다，수거량대등특점，대기진행강유처리성위고광보요감연구적일개중요문제。제출일충기우다분류기조합적고광보파단선택방법，해방법통과유전산법량호적심우능력획득약간조교우초시파단자집，재차기출상사용저사파단자집훈련약간개기분류기，진이이용개진적기우상동착오차이성도량적분류기선택방법선출부분교우분류기，실현파단선택적목적；최종통과국부정도분석적동태분류기선택실현다분류기조합결책。재공공측시수거집상적실험결과표명：여이왕직접선택최우파단자집방법상비，제출적산법능구선택경다구유감별능력적파단，명현제고료분류정학솔。
Due to the multi-waveband and massive data characteristics of hyperspectral data , dimension reduction is becoming a distinct problem in regards to hyperspectral remote sensing research .A hyperspectral band selection al-gorithm has been proposed based on a multi-classifier combination .This algorithm obtains several groups of sub-op-timal initial waveband subsets through a genetic algorithm , which has better optimizing ability and on this basis trains several base classifiers with these waveband subsets , and further selects several member classifiers from the initial classifier pool by using an improved classifier selection method that is based on same-fault measures , reali-zing the purpose of the wave band selection .And finally , the multi-classifier combination decision is made through dynamic classifier selection based on the analysis of local classification accuracy ( DCS-LA) .The experimental re-sults regarding the Indian Pine benchmark data set show that this new method can select these bands with more dis -criminative information and obviously improve the accuracy of the classification process .