提出了两种基于Gabor特征与局部保护降维的高光谱图像分类算法。该算法利用嵌入主成分分析的Gabor变换对高光谱图像进行特征提取。为了保护相邻特征的局部信息，利用局部Fisher判别分析或局部保护非负矩阵分离对Gabor特征进行降维，并采用高斯混合模型分类器对降维后的特征进行分类。两组高光谱数据的实验结果表明，本文算法不但能充分挖掘高光谱图像的谱间-空间特征，而且有效保护了高光谱图像的局部特征信息与多模型结构。与现有的几种算法相比，本文算法能得到更高的分类精度和Kappa系数，在高斯噪声环境中也具有更强的稳健性。

Two hyperspectral image classification algorithms based on Gabor features and locality-preserving dimensionality reduction are proposed. The Gabor transform is studied and implemented to extract features for hyperspectral image in the principal component analysis-projected domain. To protect locality information of neighbor features, locality Fisher discriminant analysis or locality-preserving non-negative matrix factorization is employed to reduce the dimensionality of Gabor-based feature space. The Gaussian mixture model classifier is used for classification results. Experimental results obtained from two hyperspectral datasets show that the proposed algorithms not only extract spectral-spatial features effectively, but also preserve local-feature information and multi-model structure of hyperspectral image. Compared with several existing algorithms, the proposed algorithms can obtain high classification accuracy and Kappa coefficient, and has strong robustness in Gaussian noise environment.