Reduced Set Based Support Vector Machine for Hyperspectral Imagery Classification
基于简约集支持向量机的高光谱影像分类

Aiming at the problem of more computational time needed in hyperspectral imagery classification procedure based on support vector machine, a reduced set method was brought forward to heighten hyperspectral imagery classification efficiency. The radial basis kernel function was adopted, one-against one decomposition algorithm was used to construct multi-class Support Vector Machine classifier and cross validation grid search method was applied to select model parameter. The reduced set algorithm was also used to reduce the computational complexity of predication.Through hyperspectral imagery classification experiment it can be concluded that it does not need to use all support vectors to keep generalization ability of Support Vector Machine.The reduced set algorithm can improve hyperspectral imagery classification predicative efficiency highly and keep classification accuracy at the same time.