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DOE OSTI · 1823745

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

Ren, Christopher X.·Ziemann, Amanda·Theiler, James·Durieux, Alice M.S.
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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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97 MATHEMATICS AND COMPUTINGDeep learningMachine LearningRemote Sensing

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BibTeXRIS

Ren, Christopher X., Ziemann, Amanda, Theiler, James, Durieux, Alice M.S.. 2020-05-18. Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery. https://doi.org/10.1109/ssiai49293.2020.9094603

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