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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >Proposal of Complex-Valued Convolutional Neural Networks for Similar Land-Shape Discovery in Interferometric Synthetic Aperture Radar
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Proposal of Complex-Valued Convolutional Neural Networks for Similar Land-Shape Discovery in Interferometric Synthetic Aperture Radar

机译：复数值卷积神经网络在干涉式合成孔径雷达中类似地形形状发现的建议

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摘要

We propose a complex-valued convolutional neural network to extract the areas having land shapes similar to samples in interferometric synthetic aperture radar (InSAR). InSAR extends its application to various earth observations such as volcano monitoring and earthquake damage estimation. Since the amount of data is increasing drastically in these years, it is necessary to structurize them in a big data framework. In this paper, experiments demonstrate that similar small volcanoes are grouped into a single class. We find that the neural network is capable of discovering unidentified lands similar to prepared samples successfully.
机译：我们提出了一种复值卷积神经网络，以提取具有类似于干涉合成孔径雷达（InSAR）中的样本的陆地形状的区域。 InSAR将其应用扩展到各种地球观测，例如火山监测和地震破坏估计。由于这些年来的数据量急剧增加，因此有必要在大数据框架中对它们进行结构化。在本文中，实验证明了类似的小火山被归为一类。我们发现神经网络能够成功发现与准备好的样本相似的未识别土地。

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