Adaptive Subsurface Visualization System Using Phase Retrieval Method and Complex-Valued Self-Organizing Map

S. Shimomura, A. Hirose
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引用次数: 2

Abstract

We propose an adaptive subsurface visualization system based on a complex-valued self-organizing map (CSOM). Conventionally buried things can be detected in so-called B-scan images obtained by a ground penetrating radar. In contrast, our proposed method is able not only to detect their presence, but also to classify the targets by the self-organizing dynamics in the CSOM. Instead of utilizing only the amplitude information in the time domain, we use both the amplitude and phase information to obtain the scattering coefficients of the targets by use of the phase retrieval method. The CSOM classifies the obtained scattering coefficients to realize an automatic categorization of the targets that scatter the radar electromagnetic wave.
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基于相位检索和复值自组织映射的自适应地下可视化系统
提出了一种基于复值自组织映射(CSOM)的自适应地下可视化系统。通常埋在地下的东西可以通过探地雷达获得的所谓b扫描图像来检测。相比之下,我们提出的方法不仅可以检测到目标的存在,而且可以通过CSOM中的自组织动力学对目标进行分类。在时域中,我们不再只利用振幅信息,而是同时利用振幅和相位信息,利用相位恢复方法获得目标的散射系数。CSOM对得到的散射系数进行分类,实现对雷达电磁波散射目标的自动分类。
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