摘要
本文利用AdaBoost算法对K-means算法进行提升,提出了一种基于AdaBoost算法的K-means遥感影像分类方法。其中,针对数据集分布调整的具体实施问题,设计了一种有效的加权变值方法。实验结果表明,融合提升后的分类结果较基本K-means在孤立点的消除和细长目标的识别提取上效果更加显著。
A remote sensing image classification method is presented based on AdaBoost Algorithm, which is used to boost the performance of basic K-means classifier. To solve the resampling of patterns, a weighted version is provided. Classification results produced by the boosted K-means present an obvious advantage on the elimination of isolated points and recognition of slim objects, when compared with the basic K-means.
出处
《北京电子科技学院学报》
2007年第4期70-73,共4页
Journal of Beijing Electronic Science And Technology Institute
