Abstract: 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 the
第 5 期 刘 丹，等：基于多分类器的 C5.0 决策树植被分类方法 723
proposed 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.

Key words: hyperspectral image, remote sensing, SVM, SAM, MLC, C5.0