为提高光谱伪装目标图像分类精度，提出了一种基于局部Gabor二进制模式(LGBP)的空间分类方法。LGBP作为一种多尺度算法，被用来提取高光谱图像的纹理特征。然后高光谱图像中的每一个像元可以用一个光谱特征向量及一个纹理特征向量表示。通过这种方法，增大类间距离。最后使用多核支持向量机结合光谱信息和空间纹理信息实现对高光谱伪装目标图像的分类。实验证明了该方法的有效性，分类总体精度和Kappa系数分别达到了95.6%和0.937。所提出的方法对于提高分类精度及鲁棒性具有重要意义。

A spatial classification method based on local Gabor binary patterns (LGBP) is proposed to improve the accuracy of hyperspectral camouflage targets image classification. The LGBP, a multiple scale algorithm, is employed to extract both local and global texture features of hyperspectral image (HSI). The extracted texture features have properties of gray scale invariance and rotation invariance. Each pixel is characterized by both spectral and spatial features. In this way, diversity of inter-class is enhanced. A multi-kernel support vector machine (SVM) is employed as the classifier to integrate spectral and spatial information for classification. Experiments are conducted to demonstrate the efficiency of the proposed method. The overall accuracy and Kappa coefficient of the classification reach 95.6% and 0.937 respectively. The proposed method is helpful to improve the accuracy and robustness of hyperspectral image classification.