Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
This addresses the problem of limited coverage for environmental monitoring due to clouds, offering a novel method for satellite image processing.
The paper tackled cloud occlusion in satellite images by proposing a spatiotemporal generative network (STGAN) for conditional image synthesis, achieving high PSNR and SSIM values and improving downstream tasks like land cover classification.
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.