Realistic River Image Synthesis using Deep Generative Adversarial Networks

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Authors

Akshat Gautam, Muhammed Ali Sit , Ibrahim Demir

Abstract

In this paper, we investigate an application of image generation for river satellite imagery. Specifically, we propose a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support models in surface water estimation, river meandering, wetland loss and other hydrological research studies. First, we summarized an augmented, diverse repository of overhead river images to be used in training. Second, we incorporate the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution, to generate 256x256 river satellite imagery. With conventional GAN architectures, difficulties soon arise in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seems to significantly reduce. Our preliminary results show great promise in capturing the detail of river flow and green areas present in river satellite images that can be used for supporting hydroinformatics studies.

DOI

https://doi.org/10.31223/osf.io/n5b7h

Subjects

Artificial Intelligence and Robotics, Civil and Environmental Engineering, Computer Sciences, Engineering, Environmental Engineering, Physical Sciences and Mathematics

Keywords

Deep learning, Artificial Intelligence, hydroinformatics, generative adversarial netoworks, satellite imagery

Dates

Published: 2020-02-20 11:26

Last Updated: 2020-03-02 19:51

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License

GNU Lesser General Public License (LGPL) 2.1

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