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自组织人工神经网络在立地条件遥感分类研究中的应用

APPLICATION OF SELF-ORGANIZED ARTIFICIAL NERVE NETWORK IN SITE CLASSIFICATION BY REMOTE SENSING
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摘要 运用以航空遥感图像为主的信息源,获取立地要素信息,在ARC/INFO系统支持下,建立空间信息库,选取550个样本数据,训练自组织人工神经网络,然后对159个“未知”样本进行立地分类预测和容错检验。结果表明,该模型的分类、容错能力强,综合了遥感图像目视判设与计算机自动分类的优点,开拓了遥感与GIS技术相结合进行智能化立地条件分类研究的新途径。 By using ARC/INFO system, spatial information storehouse on site condition was constructed from aerial remote sensing photo, 550 specimen was choose to train self-organized artificial nerve network, and 159 unkown sample was used to forecast the site classification and to test the ability of tolerane error.The results showed that the model possessed a powerful ability in site classification and in tolerance error,and also well synthesized the advantages of remote sensing photo interpretation by eyes and computer automatic recognition. The model opened up a new way for the intellectual site classification with the techniques combinated remote sensing and GIS.
机构地区 西北林学院
出处 《陕西林业科技》 北大核心 1998年第4期24-27,33,共5页 Shaanxi Forest Science and Technology
关键词 神经网络 立地条件类型 遥感 地理信息系统 Self-organized artificial nerve network Site type Remote sensing Geographic information system(GIS) Ningxia
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