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Proposal of Complex-Valued Convolutional Neural Networks for Similar Land-Shape Discovery in Interferometric Synthetic Aperture Radar

  • Yuki Sunaga
  • Ryo Natsuaki
  • Akira Hirose
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

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.

Keywords

Interferometric synthetic aperture radar (InSAR) Feature discovery Complex-valued neural network (CVNN) 

Notes

Acknowledgment

A part of this work was supported by JSPS KAKENHI Grant Numbers 15H02756 and 18H04105, and also by the Cooperative Research Project Program of the Research Institute of Electrical Communication (RIEC), Tohoku University. The Advanced Land Observing Satellite (ALOS) original data are copyrighted by Japan Aerospace Exploration Agency (JAXA) and provided under JAXA Fourth ALOS Research Announcement PI No. 1154.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Information SystemsThe University of TokyoTokyoJapan

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