中文摘要：

针对高光谱图像分类问题,提出了一种基于支持向量机的利用组合特征对高光谱图像进行分类的算法,组合特征综合了高光谱图像的光谱域和空域信息。针对图像的高维数据特性,利用最大噪声分量方法进行特征提取,对得到的主分量图像,采用虚拟维数估计算法来确定需要保留的主分量数目,并用数学形态学操作用保留的主分量图像中提取目标的形态信息。根据得到的空域特征并结合原始光谱域特征,构造用于分类的组合特征矢量。而且采用了支持向量机,利用了其支持小样本、效率高的优点。高光谱数据实验表明,本文提出的方法和单独使用谱域或空域信息进行分类相比,具有一定的优越性。

英文摘要：

A hyperspectral image classification algorithm based on support vector machine（SVM） is presented.The hybrid features are made up of the spectral domain and spatial domain information of the image.In order to deal with high dimensional data of the hyperspectral image,a maximum noise fraction（MNF） extraction method is adopted.A virtual dimensionality（VD） estimation algorithm is used to determine the number of the principal components（PC） remained.The morphology profiles（MP） of the object are extracted from the remained PC images by using mathematical morphology method.The hybrid features for image classification are constructed by combining the spatial characteristics with spectral domain characteristics of the original image.Because of its high efficiency and small training samples,the SVM is adopted as the classifier.The superiors of the proposed method compared with the classifiers only using spectral or spatial information are shown by experiments.