提出了一种空-谱二维特征蚁群组合优化支持向量机的高光谱图像分类算法。 利用两类蚁群分别在光谱维空间和样本分布空间交替搜索最大类间距波段组合和异质样本, 提取最优特征波段, 降低了高光谱的波段信息冗余, 去除训练样本中的异质样本, 优化了训练样本特征空间分布。 将蚁群组合优化后的高光谱图像和训练样本应用到支持向量机(SVM)分类器中, 扩大了特征空间类间距, 提高了SVM算法的分类精度。 实验表明该算法总分类精度达95.45%, Kappa系数0.925 2, 是一种分类精度较高的高光谱图像分类方法。

A novel classification algorithm of hyperspectral imagery based on ant colony compositely optimizing support vector machine in spatial and spectral features was proposed. Two types of virtual ants searched for the bands combination with the maximum class separation distance and heterogeneous samples in spatial and spectral features alternately. The optimal characteristic bands were extracted, and bands redundancy of hyperspectral imagery decreased. The heterogeneous samples were eliminated form the training samples, and the distribution of samples was optimized in feature space. The hyperspectral imagery and training samples which had been optimized were used in classification algorithm of support vector machine, so that the class separation distance was extended and the accuracy of classification was improved. Experimental results demonstrate that the proposed algorithm, which acquires an overall accuracy 95.45% and Kappa coefficient 0.925 2, can obtain greater accuracy than traditional hyperspectral image classification algorithms.