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针对传统SVM分类方法的缺点,采用粒子群优化(particle swarm optimization,PSO)算法自动选择合适的波段影像并对SVM核函数参数进行优化,提出一种新的PSO-BSSVM分类模型。经过对高光谱遥感影像的分类试验,并与K-最近邻(K-NN)、径向基神经网络(RBF-NN)和标准的支持向量机(SVM)三种分类方法进行对比实验,证明PSO-BSSVM方法能优选高光谱遥感影像的波段和优化SVM参数,明显提高影像的分类精度。 更多还原

【Abstract】 It is proposed an automatic band selection and SVM parameter optimization method based on a novel PSO-BSSVM model,which is used to classify the hyperspectral remote sensing images.Comparing with K-nearest neighbors classifier(K-NN),radial basis function-neural network(RBF-NN)classifier and standard SVM classifier,the empirical results have demonstrated that PSO-BSSVM can automatically select appropriate hyperspectral bands and optimize SVM parameters,and the classification accuracy of hyperspect... 更多还原