Proposal of complex-valued convolutional neural networks for similar land-shape discovery in interferometric synthetic aperture radar

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.