Aiming at the problem that the vegetation classification accuracy of spectral angle mapping (SAM) and maximum likelihood classifier (MLC) for AVIRIS hyperspectral remote sensing images both are low, a vegetation classification method based on C5.0 decision tree of multiple classifiers is proposed. During the first stage, the kernel function and its parameters are selected by using support vector machine (SVM) to extract the vegetation information of AVIRIS hyperspectral image. Then, SAM and MLC combined by C5.0 algorithm, as characteristic property of the decision tree, are used to learn samples training and generate classification rules. According to the C5.0 algorithms, the information gain rate of the corresponding classifier in the vegetation samples is calculated, and the attributes with the largest information gain rate are selected to classify the samples. While classification results of leaf sample meeting the stopped growing threshold, outputs the result of sample classification, otherwise, go back to the beginning, recursively tune the above method and goes on classifying leaf sample, until all the subset contains only the sample of a vegetation type, and complete decision. Experiments show that comparing with SAM and MLC, overall accuracy of theproposed method were improved by 6.04%, 2.92%, respectively, not only confirmed the feasibility and effectiveness of the combination of multiple classifiers, and it is more suitable for vegetation investigation in the AVIRIS hyperspectral image.%针对光谱角制图(SAM)和最大似然(MLC)分类器对AVIRIS高光谱遥感图像进行植被分类精度均不高的问题,提出了一种基于多分类器的C5.0决策树植被分类方法.首先,利用支持向量机(SVM),进行核函数以及核函数参数选择,提取出AVIRIS高光谱图像中的植被信息.其次,利用C5.0算法将光谱角制图和最大似然分类器组合,作为决策树的特征属性,学习样本训练并生成分类规则;根据C5.0算法计算植被样本中对应分类器的信息增益率,选择信息增益率最大的属性去分类样本;当叶样本的分类结果满足停止生长的阈值,输出样本分类的结果,否则,回到开始,递归调用以上方法继续分类叶样本,直到所有子集仅包含一个植被类别的样本完成决策.实验结果表明,与光谱角制图和最大似然分类器相比,本文提出的方法整体精度分别提高了6.04%、2.92%,不仅证实了多分类器组合的可行性和有效性,而且更加适用于AVIRIS高光谱图像中的植被调查.
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