自组织神经网络在遥感影像分类中的应用研究

doi:10.6046/gtzyyg.2004.04.05

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摘要竞争学习网络与Kohonen神经网络相比,由于不考虑邻域神经元,其网络结构相对简单。采用这种简化的网络结构,并对其学习算法进行改进,用最大、最小距离法设置的初始聚类中心来代替随机初始中心。实验结果表明,用改进的竞争学习网络对遥感影像进行非监督分类,在分类精度和效率上都有较大的提高。

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Abstract：In comparison with the Kohonen neural networks, the structure of the compete study networks is relatively simple because it does not consider neighboring neural units. In experiments, the authors adopted this simplified neural structure and improved its study algorithm by using max-min distance means. Experimental results show that the classification accuracy and efficiency of the improved compete study networks are remarkably raised in unsupervised classification of remote sensing imagery, and hence the technique of compete study networks has the practical application value.

收稿日期: 2004-04-12 出版日期: 2011-08-02

基金资助:

国家高技术研究发展计划(863计划;项目编号:2001AA13510)。

作者简介: 刘修国(1969-),中国地质大学信息工程学院教授,主要从事空间数据库及遥感图像处理研究.

引用本文:

刘修国, 罗小波. 自组织神经网络在遥感影像分类中的应用研究[J]. 国土资源遥感, 2004, 16(4): 14-18.
LIU Xiu-guo, LUO Xiao-bo . THE APPLICATION OF SELF-ORGANIZING NEURAL NETWORKS TO REMOTE SENSING IMAGERY CLASSIFICATION. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(4): 14-18.

链接本文:

https://www.gtzyyg.com/CN/10.6046/gtzyyg.2004.04.05 或 https://www.gtzyyg.com/CN/Y2004/V16/I4/14

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