提出了两种基于主成分分析与局部二值模式的高光谱图像分类算法。利用主成分分析去除高光谱图像的谱间冗余信息，对降维后的图像利用局部二值模式进行空间纹理特征分析，采用稀疏表示分类和支持向量机分别对提取的特征进行分类。其通过将主成分分析与局部二值模式相结合对高光谱图像进行特征提取，保证了高光谱图像的谱间冗余的有效去除，同时保护了高光谱图像的空间局部邻域信息，因此，此类算法不但能充分挖掘高光谱图像的谱间-空间特征，在较大程度上提高分类精度和Kappa系数，而且在高斯噪声环境中和小样本情况下也具有良好的分类性能。

Two kinds of hyperspectral image classification algorithms based on principal component analysis and local binary patterns are proposed. The principal component analysis is employed to reduce the redundant information in spectral domain. Following that, the local binary patterns are studied to analyze the spatial texture features. And the sparse presentation classification and support vector machine are used for a classification of extracted results, respectively. Combining the principal component analysis with the local binary patterns for extracting the features of hyperspectral image, we ensure that the spectral redundant information is reduced effectively, and the spatial local neighborhood information is protected. Hence, the proposed algorithms can not only sufficiently excavate spectral-spatial features of hyperspectral image for improving classification accuracy and Kappa coefficient, but also have outstanding classification performance in Gaussian noise environments and small-sample-size condition.