中文摘要：

为了提高高光谱遥感图像分类中空间信息的利用率,提出一种将空间邻域信息和光谱信息结合的组合核支持向量机（SVM）学习算法.用SVM进行预分类,从分类结果图提取各像素的空间邻域特征,与光谱特征结合构造组合核SVM进行分类,并再次提取空间邻域特征进行多次空-谱信息组合核SVM迭代分类,如此迭代10次,从中选择合适的结果作为最终输出.结果表明,该方法对传统支持向量机的分类精度提升幅度可达10%左右.同时,与其他组合核支持向量机相比,该算法用更少的训练样本获得了更高分类精度.

英文摘要：

To improve the utilization of spatial information when classifying hyperspectral images,this paper proposes a composite kernel SVM algorithm combining spatial and spectral information.First,the hyperspectral image was classified into a map using conventional SVM.The spatial-contextual features were then extracted based on the classified map,and combined with spectral information to construct a composite kernel SVM for classification.The spatial-contextual features were extracted again and the composite kernel SVM classified the image iteratively.The process was repeated 10 times and a proper one was chosen as the last outcome.The results show that the method increases the overall accuracy by around 10%,compared with conventional SVM.In addition,the method also demands much less training samples than usual SVM.