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基于改进型模糊ARTMAP网络的CBR遥感图像分类系统研究

DOI: 10.11834/jig.20070613

Keywords: 范例推理,遥感,分类,样本重用

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Abstract:

针对难以及时地获取充足而准确的遥感样本、缺乏积累和管理遥感样本的有效手段是制约遥感图像分类技术发展的瓶颈问题。构建了基于改进型模糊ARTMAP网络的CBR(case-basedreasoning范例推理)遥感图像分类系统。系统将改进型模糊ARTMAP网络作为范例的知识提取器和图像分类器,运用CBR求解策略实现遥感样本知识的合理储备、优化组合和重复利用。分别应用本文所建系统、最大似然法、BP网络和改进型模糊ARTMAP网络对向海自然保护区TM遥感图像进行分类操作,实验结果表明,本文建立的系统与其他分类方法相比,能够更好地提高遥感样本数据的利用效率和遥感图像的分类精度,而且一定程度上解决了在样本有限的条件下如何高效利用已有数据进行遥感图像分类的问题。

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