CAJ | 학술논문

地面岩性波谱建模是高光谱遥感地质填图的关键环节之一，地面岩性波谱具有高维、多类特点，传统的多光谱分类方法不能对地面岩性高光谱数据进行有效处理。该文在岩性波谱特征分析的基础上，构建岩性波谱特征空间；然后，从支持向量机（SVM）基本理论出发并对算法改进，建立了一个基于决策树的SVM多类分类器（DT-SVMs）；最后，利用云南北衙金矿区采集的野外实测岩性波谱数据进行分类实验，分类正确率达到93.75％。实验结果表明：基于决策树的多类分类支持向量机（DT-SVMs）可以很好的应用于地面岩性的波谱分类建模。同时，可推广到高光谱遥感岩性分类研究。
지면암성파보건모시고광보요감지질전도적관건배절지일，지면암성파보구유고유、다류특점，전통적다광보분류방법불능대지면암성고광보수거진행유효처리。해문재암성파보특정분석적기출상，구건암성파보특정공간；연후，종지지향량궤（SVM）기본이론출발병대산법개진，건립료일개기우결책수적SVM다류분류기（DT-SVMs）；최후，이용운남북아금광구채집적야외실측암성파보수거진행분류실험，분류정학솔체도93.75％。실험결과표명：기우결책수적다류분류지지향량궤（DT-SVMs）가이흔호적응용우지면암성적파보분류건모。동시，가추엄도고광보요감암성분류연구。
The methods of multispectral imagery processing,such as artificial neural network classifica-tion and independent component analysis are not applicable to high spectral imagery,because of“Hughes Phenomenon”(when training samples are fixed,the classification accuracy decreases with the increase of feature dimension)and classification accuracy under small study samples can not be effectively solved for high dimensional data.A decision-tree-based multiclass support vector machines is proposed and applied in spectral classification for the multi-classification problem of surface rocks collected from the Beiya gold mine,Heqing County,Yunnan Province.The results show that the average classification accuracy rate can be above 93.75%,suggesting that multiple classification based on decision tree classification support vector machine (SVM)can be applied to spectral classification of rocks.