基于决策树分类器的遥感影像植被分类方法研究

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI:10.1109/SSCI.2018.8628721

Wei Wei, Dawid Połap, Xiaohua Li, M. Woźniak, Junzhe Liu

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引用次数: 14

摘要

针对森林植被分类不准确的问题，研究了基于影像的遥感方法。作为分类器，我们使用了基于Boost树、Ada树和C5方法的决策树方法。对于实验，我们使用单决策树生成方法，其中训练元组是通过抽样生成的。在实验中，我们试图评估分类对农业图像的作用。

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Study on Remote Sensing Image Vegetation Classification Method Based on Decision Tree Classifier

Aiming at the problem of inaccurate classification of forest vegetation, this paper presents a study on possible method for remote sensing from images. As classifiers we have used decision tree methods based on the idea of Boost Tree, Ada Tree and C5 approaches. For the experiments we have used single decision tree generation method for which training tuples are generated by sampling. In experiments we tried to evaluate how classifications work for agriculture images.

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2018 IEEE Symposium Series on Computational Intelligence (SSCI)

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