中文摘要：

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.

英文摘要：

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.