CAJ | 학술논문

针对遥感影像分类过程中混合像元难判别的问题，提出一种基于 Gustafson-Kessel 模糊聚类算法的支持向量机(SVM)分类模型。以 Gustafson-Kessel 算法优选训练样本方式提高支持向量机的分类性能。为验证其有效性，将该模型应用于森林覆盖类别分类，并与标准支持向量机模型分类结果对比。实验结果表明，该方法能提高支持向量机对混合像元划分的精度。
침대요감영상분류과정중혼합상원난판별적문제，제출일충기우 Gustafson-Kessel 모호취류산법적지지향량궤(SVM)분류모형。이 Gustafson-Kessel 산법우선훈련양본방식제고지지향량궤적분류성능。위험증기유효성，장해모형응용우삼림복개유별분류，병여표준지지향량궤모형분류결과대비。실험결과표명，해방법능제고지지향량궤대혼합상원화분적정도。
In view of a lot of mixed image pixels contained in remote sensing images classification, fuzzy clustering support vector machine (SVM)was introduced to deal with the remote sensing images unmixing.In the proposed technique,Gustafson-Kessel is used to select the useful sample points for improving the classification performance of support vector machine.The effectiveness of the proposed method was evaluated through the forest cover remote sensing classification.The experiment shows that the accuracy of mixed pixels classification can be increased by applying the learning scheme,compared with that of traditional SVM classification method.