关键词: 高光谱, 分类, 光谱吸收特征参数, 决策树, 朴素贝叶斯, 朴素贝叶斯树

Abstract: In this paper，we studied the hyperspectral classification with missing attribute values adopting decision tree C4.5，Naive Bayes and NBTree on the Weka platform.As the large hyperspectral data have numerous wave bands and high information redundancy，firstly，we extracted the spectral feature parameters from the spectral curve and choosed the right wave bands of the absorption peaks as input vector.Experimental results indicate that the classification error of uranium-uraninite classification model based on NBTree is minimum；also it has the best classification accuracy compared with Naïve Bayes and J4.8. However，NBTree needs more training time than Naïve Bayes and J4.8. Finally，we analyzed the results of the three classification algorithms.

Key words: hyperspectral, classification, spectral absorption feature parameters, decision tree, Naive Bayes, Naive Bayes Tree