中文摘要：

为改善面向对象的SPOT5遥感图像森林分类精度，在多分类器结合的投票法、Bayesian平均法和模糊法的基础上提出综合保守投票法和模糊法的多分类器结合新方法——投票～模糊法。对最小距离、马氏距离、Bayes准则、模糊逻辑和支持向量机5个分类器结合的试验检测结果表明：采用投票一模糊进行分类器结合，总体分类精度和Kappa系数均比投票法、Bayesian法和模糊法的高，也略高于分类效果最好的单个分类器——Bayes分类器，且各类型间生产者精度的差异减小。但分类器结合效果不很明显，其主要原因可能是各分类器采用同一套训练样本，分类器输出结果之间存在较高的关联性，并且所分类型较多。因此，在实际应用中，应尽可能确保各个分类器训练样本的差异性，或者尽可能地避免每个分类器都采用相同的对象特征。

英文摘要：

Aimed toward improve the accuracy of object-oriented classification using SPOT5 imagery, three mtdticlassifier combination methods, include voting rule, Bayesian mean and fuzzy fusion rule, were discussed and a new fusion approach named voting - fuzzy rule was developed, which synthesized conservative voting rule and fuzzy fusion rule. Five classifiers include minimums distance, Mahalanobis distance, Bayes rule, fuzzy logic and support vector machine, were involved in the combination. The result indicated that the voting - fuzzy rule had higher total accuracy and Kappa index than three other combination rules, and also the Bayes rule, the best single classifier in all classifiers; furthermore, it reduced the difference of producer accuracy between classes. However, the combination effect wasn＇ t as obvious as indicated in literatures. The reason might owe to the high eorrelativity in the outputs of five classifiers, for them shared with a sample set, and the protocol with twenty two classes. Thus classifiers shouldn＇ t be trained with a same training sample set, or might select difference object features in practical application.