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Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

2021-05-16 00:57:43
Arthita Ghosh, Max Ehrlich, Larry Davis, Rama Chellappa
arXiv_CV
           
Abstract

Urban material recognition in remote sensing imagery is a highly relevant, yet extremely challenging problem due to the difficulty of obtaining human annotations, especially on low resolution satellite images. To this end, we propose an unsupervised domain adaptation based approach using adversarial learning. We aim to harvest information from smaller quantities of high resolution data (source domain) and utilize the same to super-resolve low resolution imagery (target domain). This can potentially aid in semantic as well as material label transfer from a richly annotated source to a target domain.

Abstract (translated)

URL

https://arxiv.org/abs/2105.07322

PDF

https://arxiv.org/pdf/2105.07322.pdf

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