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Cloud Removal

The majority of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, persistent cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. Cloud removal is the task of reconstructing cloud-covered information while preserving originally cloud-free details.

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Papers

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Showing 41–50 of 56 papers

TitleStatusHype
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images—0
Multimodal Diffusion Bridge with Attention-Based SAR Fusion for Satellite Image Cloud Removal—0
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation—0
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites—0
Patch-GAN Transfer Learning with Reconstructive Models for Cloud Removal—0
Reading Industrial Inspection Sheets by Inferring Visual Relations—0
Removing cloud shadows from ground-based solar imagery—0
SAR-to-RGB Translation with Latent Diffusion for Earth Observation—0
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization—0
When Cloud Removal Meets Diffusion Model in Remote Sensing—0
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