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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
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translationCode1
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal—0
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial NetworkCode1
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 ImageryCode1
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization—0
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusionCode1
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks—0
Cloud Removal in Satellite Images Using Spatiotemporal Generative NetworksCode1
A Remote Sensing Image Dataset for Cloud RemovalCode0
Reading Industrial Inspection Sheets by Inferring Visual Relations—0
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