摘要
针对数量激增、数据类型复杂的遥感影像,准确和具有普适性的分类是亟待解决的问题。提出一种轮转径向基函数神经网络模型应用于遥感影像的处理方法。通过对输入数据的特征变换,使特征总集变为多个子特征集,依据PCA(主成分分析)变换处理这些新的子特征集,将得到的系数用于改变训练样本,增加基分类器之间的差异度,提高分类精度。以扎龙湿地为研究对象将该算法与其他方法比较,结果显示本文方法能得到更准确的分类结果,而且具有较高的泛化精度以及较小的过学习现象。
The amount of remotely sensed data increases rapidly, and the information contained in this data becomes more and more complicated, the way how to classify these datasets generalized and effectively is a problem which needs urgently to be solved. A modified rotation forest algorithm is proposed which takes the RBFNN as the base classifier to classify the remote sensing image. The input training dataset is changed by the rotation forest which can output a much small sub- feature. Then the non-redundancy feature set is got by using PCA technology to process these new sub-features. Finally, the training dataset changes according to the coefficient by the PCA transformation. This change will lead a higher diversity factor among these sub-classifiers which will give a much higher accuracy. The proposed method can obtain higher classification accuracy than other traditional methods when it used on the Zhalong wetland remote sensing image, and this algorithm has much higher generalization ability and much less over study phenomenon.
出处
《中国图象图形学报》
CSCD
北大核心
2011年第11期2024-2029,共6页
Journal of Image and Graphics
基金
国家自然科学基金项目(61074096)
国家科技支撑计划项目(2006BAB14B05)
