关键词: 独立分量分析, 支持向量机, 高光谱, 概率神经网络, 混合核函数

Abstract: The traditional Independent Component Analysis（ICA） method can’t determine the number of Independent Components （ICs） in hyperspectral data，but it can quickly determine the number of ICs by using the advantage of short training time in Probability Neural Network（PNN）.A combined method of ICA，PNN and Support Vector Machine（SVM） is proposed for hyperspectral data classification.Use ICA to do dimensionality reduction for hyperspectral dada at first，and then use PNN to determine the number of ICs，at last use SVM with mixture kernels to do classification for the dimensionality reduction data.By the experiments，this method can insure the classification accuracy while reducing the time of classification.

Key words: independent component analysis, support vector machine, hyperspectral, probability neural network, mixture kernels