SOTAVerified

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.

Image Source: URL

Papers

Showing 1120 of 56 papers

TitleStatusHype
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite ImageryCode1
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusionCode1
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial NetworkCode1
Image Restoration via Multi-domain LearningCode1
Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention NetworkCode1
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 ImageryCode1
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translationCode1
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local FusionCode1
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite ImagesCode1
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud RemovalCode1
Show:102550
← PrevPage 2 of 6Next →

No leaderboard results yet.