一种高光谱图像的半监督分类方法

A Semi-supervised Classification Method for Hyperspectral Images

基于密度的抽样和动态时间扭曲距离,提出了一种半监督高光谱模糊聚类方法。该方法首先应用基于密度的方法对样本进行抽样,然后采用动态时间扭曲距离计算样本之间的相似度,最后利用半监督模糊C均值算法进行聚类。为了验证所提出方法的有效性,在广泛使用的Indian Pines数据集和Pavia U数据集上进行试验。结果表明,本文提出的方法能够取得理想的分类结果。

Based on density sampling and dynamic time warping distance,this paper proposes a semi-supervised fuzzy cluster method to partition hyperspectral data set into several groups. The labeled samples are first obtained by density sampling method. Then the dynamic time warping distance is computed between a pair of samples. Lastly,semi-supervised fuzzy c-means is employed to cluster the hyperspectral image. To validate the proposed method,the Indian Pines and Pavia U data sets are chosen to feed our method. The experimental results show that it can discover the ideal clusters by the proposed method.