高光谱图像分类是高光谱数据分析的重要研究内容.相关向量机由于不受梅西定理的限制、不需要设置惩罚因子等优势受到广泛关注.由于高光谱数据具有较高的维数, 当训练样本较少时, 高光谱数据的分类精度受到严重的影响.通常解决这种现象的办法是对原数据进行特征降维处理, 然而多数基于filter模型的特征选择算法无法直接给出最优特征选择个数.为此提出利用蒙特卡罗随机实验可以对特征参量进行统计估计的特性, 计算高光谱图像的最优降维特征数, 并与相关向量机结合, 对降维后的数据进行分类.实验结果表明了使用蒙特卡罗算法求解降维波段数的可靠性.相比较原始未降维数据, 降维后的高光谱图像分类精度有较大幅度的提高.

Hyperspectral image classification is an important research aspect of hyperspectral data analysis. Relevance vector machine (RVM) is widely utilized since it is not restricted to Mercer condition and does not have to set the penalty factor. Due to the high dimension of hyperspectral data, the classification accuracy is severely affected when there are few training samples. Feature reduction is a common method to deal with this phenomenon. However, most of the filter model based feature selection methods can not provide optimal feature selection number. This paper proposes to utilize the statistic estimation characteristic of Monte Carlo random experiments to calculate optimal feature reduction number and conduct hyperspectral image classification with relevance vector machine. Experimental results show the reliability of the feature reduction number calculated by Monte Carlo method. Compared with the classification of original data, there is a significant improvement in the classification accuracy with the feature reduction data.