Cloud Removal from Satellite Images using Spatiotemporal Generator Networks

Vishnu Sarukkai, Anirudh Jain, Burak Uzkent, Stefano Ermon; The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 1796-1805

Abstract


Satellite images hold great promise for continuous environmental monitoring and earth observation. Occlusions cast by clouds, however, can severely limit coverage, making ground information extraction more difficult. Existing pipelines typically perform cloud removal with simple temporal composites and hand-crafted filters. In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds. We train our model on a new large-scale spatiotemporal dataset that we construct, containing 97640 image pairs covering all continents. We demonstrate experimentally that the proposed STGAN model outperforms standard models and can generate realistic cloud-free images with high PSNR and SSIM values across a variety of atmospheric conditions, leading to improved performance in downstream tasks such as land cover classification.

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[bibtex]
@InProceedings{Sarukkai_2020_WACV,
author = {Sarukkai, Vishnu and Jain, Anirudh and Uzkent, Burak and Ermon, Stefano},
title = {Cloud Removal from Satellite Images using Spatiotemporal Generator Networks},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}