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Structurization of synthetic aperture radar information by using neural networks

机译:用神经网络结构化合成孔径雷达信息

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Recent earth observation data gathered by satellite-borne synthetic aperture radar (SAR) systems grow vei7 rapidly. Previously we proposed the structurization of the gathered data into an easy-to-use form for extensive utilization of the data. There we employ convolutional neural networks to extract lands shape features to realize automatic metrization of local area patches based on teiTain features obtained as SAR images. This paper reviews successful metrization in the proposal, which leads to total big-SAR-data structurization.
机译:最近由卫星合成孔径雷达(SAR)系统收集的地球观测数据迅速生长vei7。以前,我们提出了聚集的数据的结构化成易于使用的形式,以广泛利用数据。在那里,我们采用卷积神经网络来提取土地形状特征,以实现基于作为SAR图像获得的Teitain特征来实现局域斑块的自动化。本文审查了该提案中成功的重定化,从而导致总重大SAR数据结构化。

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