国土资源遥感Issue(2)：87-92,6.DOI:10.6046/gtzyyg.2014.02.15
基于随机森林算法的高维模糊分类研究
Study of high-dimensional fuzzy classification based on random forest algorithm
张修远 1刘修国1
作者信息
- 1. 中国地质大学 武汉 信息工程学院，武汉 430074
- 折叠
摘要
Abstract
The spatial resolution of hyperspectral data is generally very low, the mixed pixels are extensively distributed, and hence fuzzy classification is commonly used in the mixed pixel analysis. As the accuracy of fuzzy classification is often limited by the feature dimensions and fuzzy samples selection, the random forest ( RF ) algorithm is put forward in this paper to select features and obtain fuzzy samples; in the low-dimensional feature space, fuzzy samples are used to make fuzzy classification. Fuzzy classification and RF are merged by using two-step classification,following the principle of unanimity assumption. Using different samples,different experimental areas and different partition optimization situations,the authors conducted three comparative experiments, and the results show that the method proposed in this paper solves the limitation of fuzzy classification and improves its accuracy. It is also proved that the classification accuracy of the method is robust for the original sample.关键词
随机森林( RF)/模糊分类/高维特征Key words
random forest( RF)/fuzzy classification/high dimensional features分类
信息技术与安全科学引用本文复制引用
张修远,刘修国..基于随机森林算法的高维模糊分类研究[J].国土资源遥感,2014,(2):87-92,6.
