光学精密工程2011，Vol.19Issue(4)：878-883,6.DOI:10.3788/OPE.20111904.0878
组合核函数支持向量机高光谱图像融合分类
Fusion classification of hyperspectral image by composite kernels support vector machine
摘要
Abstract
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.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Key words
hyperspectral image/ image fusion/ mathematical morphology/ composite kernel/ Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
高恒振,万建伟,粘永健,王力宝,徐湛..组合核函数支持向量机高光谱图像融合分类[J].光学精密工程,2011,19(4):878-883,6.基金项目
国家自然科学基金资助项目(No.40901216) （No.40901216）
湖南省研究生科研创新项目(No.CX2010B020) （No.CX2010B020）
国防科技大学博士研究生创新基金资助项目(No.B100402) （No.B100402）
