| 基于高光谱吸收特征参数的分类研究 |
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| 引用本文： | 陈文霞,陈安升,蔡之华.基于高光谱吸收特征参数的分类研究[J].计算机工程与应用,2008,44(28):230-232. |
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| 作者姓名： | 陈文霞 陈安升 蔡之华 |
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| 作者单位： | 中国地质大学,计算机学院,武汉,430074 |
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| 摘 要： | 在Weka平台上,采用决策树C4.5、朴素贝叶斯、朴素贝叶斯树三种算法进行了带缺失属性值的高光谱分类研究。针对高光谱波段数众多、信息冗余量大的特点,首先对光谱曲线进行光谱特征参数提取,然后再选择合适的吸收峰波段作为输入向量来进行分类。实验表明,由NBTree建立的铀黑-沥青铀矿分类模型的分类误差最小,分类精度最高,其次是NaveBayes和J4.8,但从训练时间来看,NBTree则高于NB和J4.8。最后,对三种分类算法的分类结果进行了分析。
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| 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 |
| 收稿时间： | 2007-11-14 |
| 修稿时间： | 2008-2-22
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Research on hyperspectral absorption feature parameters based classification |
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| CHEN Wen-xia,CHEN An-sheng,CAI Zhi-hua.Research on hyperspectral absorption feature parameters based classification[J].Computer Engineering and Applications,2008,44(28):230-232. |
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| Authors: | CHEN Wen-xia CHEN An-sheng CAI Zhi-hua |
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| Affiliation: | School of Computer Sciences，China University of Geosciences，Wuhan 430074，China |
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| 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. |
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| Keywords: | hyperspectral classification spectral absorption feature parameters decision tree Naive Bayes Naive Bayes Tree |
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