针对高光谱图像中同质异谱现象造成的分类精度较低以及边缘像元在联合空间光谱信息分类时特征易混淆的问题，提出了基于分层引导滤波与最近邻正则化子空间的分类方法.利用主成分分析获得高光谱图像的第一主成分.以第一主成分为引导图像对高光谱图像执行分层引导滤波操作，引导滤波的边缘保护特性，有效阻隔了边缘处类间光谱信息的混淆，并减小了局部区域类内光谱的差异，最后将预处理后的高光谱图像送至最近邻正则化子空间分类器进行分类识别.在Indian Pines，Salinas以及GRSS_DFC_2013高光谱数据集上与现有的方法进行对比实验.结果表明，本文提出的方法在三个数据集上分别取得了98.63%，99.13%与99.42%的总体分类准确率，有着更优的分类精度与可视化效果.

Aiming at the problems of low classification accuracy caused by the phenomenon that homogeneous pixels have different spectrum in the hyperspectral image and the characteristics of edge pixels being easily confused when combining spatial and spectral information, a method based on hierarchical guidance filtering and nearest regularized subspace is proposed in this paper. Firstly, the principal component of the hyperspectral image is obtained by principal component analysis, and then the hierarchical guidance filtering is performed with the guidance image, the first principal component. The edge-preserving characteristic of the guided filtering, effectively prevents the mixing of spectral information in edge area, and reduces the difference of the homogeneous spectrum at local regions. Finally, the nearest regularized subspace classifier is applied to classify the preprocessed hyperspectral image. Compared with the existing methods on Indian Pines, Salinas and GRSS_DFC_2013 hyperspectral datasets, the results show that the method proposed in this paper has achieved overall classification accuracy of 98.63%, 99.13% and 99.42% on the three datasets respectively, with better classification accuracy and visualization.