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arXiv · 1911.12546

Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery

Christopher X. Ren·Amanda Ziemann·Alice M. S. Durieux·James Theiler
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Abstract

This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings. The method, a cycle-consistent adversarial network (CycleGAN), requires low quantities of training data to generate realistic changes. Here we show an application of CycleGAN in creating realistic snow-covered scenes of multispectral Sentinel-2 imagery, and demonstrate how these images can be used as a test bed for anomalous change detection algorithms.

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Christopher X. Ren, Amanda Ziemann, Alice M. S. Durieux, James Theiler. 2019-11-28. Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery. https://arxiv.org/abs/1911.12546

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