【摘要】 遥感影像分类一直是遥感领域的研究热点。集成学习通过多个单一分类器得到的分类信息进行综合来提高分类的精度。论文阐述了集成技术的常用算法和策略,给出了遥感数据分类采用单分类算法,Bagging,Boosting以及MCS集成分类的实验结果的比较和分析。实验表明,集成技术能有效提高遥感数据的分类精度。在训练样本少的情况下,提供了一种保证分类性能和泛化性的有效途径。更多还原

【Abstract】 Remote sensing image classification has been the research focus in the field of remote sensing.Ensemble learning trains multiple component learners and then combines their predictions to enhance the accuracy of classification.The paper explains the ensemble algorithms and strategies,and the comparison and analysis of classification results are given which obtain by employing the single classification algorithm,Bagging,Boosting and MCS ensemble classifiers for the given remote sensing data.The experimental results show that the ensemble technology can effectively improve the performance of remote sensing data classification.Even under the fewer number of the training examples,it provides a kind of effective approach to guarantee the performance and generalization of classification.更多还原