When aiming at hyperspectral classification, spectral characteristics are often considered, while spatial characteristics and information redundancy between spectra are neglected. We propose a method of feature extraction using a uniform local binary pattern and ensemble multiple models to classify features. This model uses a uniform local binary mode to extract the spatial features of hyperspectral images. The spatial features and the spectral features are fused to obtain high-dimensional fusion data, which are input into the sparse representation model for learning, and the residual of the test sample is obtained. At the same time, hyperspectral images have serious information redundancy, so we use the product moment correlation coefficient to reduce the interference between classes of sample information. Finally, through ensemble learning of different classification models, the hyperspectral classification can be accurately predicted. The experimental results on hyperspectral data show that this method can effectively improve the effect of hyperspectral image classification.

当针对高光谱分类时，经常考虑光谱特征，而光谱之间的空间特征和信息冗余被忽略。我们提出了一种使用统一的局部二进制模式和集合多个模型对特征进行分类的特征提取方法。该模型使用统一的本地二进制模式提取高光谱图像的空间特征。将空间特征和光谱特征融合以获得高维融合数据，将其输入到稀疏表示模型中进行学习，并获得测试样本的残差。同时，高光谱图像具有严重的信息冗余，因此我们使用乘积矩相关系数来减少样本信息类别之间的干扰。最后，通过不同分类模型的集成学习，可以准确预测高光谱分类。高光谱数据的实验结果表明，该方法可以有效提高高光谱图像分类的效果。