摘要
针对高光谱图像分类,提出了一种利用组合核函数融合目标光谱域和空域信息的支持向量机学习算法。该算法首先用主成分分析方法对高光谱图像进行特征提取和降维,用虚拟维数估计策略预估原始图像的本征维数,并且在预估的基础上确定要保留的主成份分量数目;然后用数学形态学操作在选取的主分量图像上提取目标的形态信息,得到扩展的空域形态矢量。最后,通过不同的组合策略,构造组合核函数,从而在分类器中引入空域信息,和原有的谱域信息一起,利用支持向量机进行分类。高光谱数据实验表明,在训练时间没有显著差别的情况下,总体分类精度和Kappa系数均提高了2%左右。实验表明,本文提出的方法较单独使用谱域或空域信息进行分类具有一定的优越性。
For hyperspectral image classification,a Support Vector Machine（SVM） algorithm with composite kernels was presented to fuse both the spectral information and spatial information of the image.The algorithm adopts Principal Component Analysis（PCA） algorithm to extract the image feature and reduce the dimension for hyperspectral image,and uses the Virtual Dimension（VD） algorithm to estimate the Intrinsic Dimension（ID） of the image.Then,the remained number of Principal Components（PCs） was determined on the basis of the ID.Furthermore,spatial features were extracted by mathematical morphology from the remained PCs,and the Extended Morphological Profile（EMP） vector of image was obtained.By combination of different strategies to construct composite kernels,the spatial information was introduced into the classifier to implement the classification with the SVM and based on both the spectral information and spatial information.Hyperspectral image experiments indicate that the overall accuracy and Kappa coefficients of the proposed approach increase about 2% without increasing the training time obviously.Compared with the classifiers only using the spatial or spectral information,the proposed method shows a lot advantages.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2011年第4期878-883,共6页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.40901216)
湖南省研究生科研创新项目(No.CX2010B020)
国防科技大学博士研究生创新基金资助项目(No.B100402)
关键词
高光谱图像
图像融合
数学形态学
组合核函数
支持向量机
hyperspectral image
image fusion
mathematical morphology
composite kernel
Support Vector Machine（SVM）
