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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 1–50 of 56 papers

TitleStatusHype
Effective Cloud Removal for Remote Sensing Images by an Improved Mean-Reverting Denoising Model with Elucidated Design SpaceCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing ImageryCode2
Multi-Modal and Multi-Resolution Data Fusion for High-Resolution Cloud Removal: A Novel Baseline and BenchmarkCode1
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing ImagesCode1
Image Restoration via Multi-domain LearningCode1
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial NetworkCode1
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical ModelCode1
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time SeriesCode1
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud RemovalCode1
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translationCode1
AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite ImageryCode1
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
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time SeriesCode1
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite ImagesCode1
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention NetworksCode1
PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite ImageryCode1
Cloud Removal in Satellite Images Using Spatiotemporal Generative NetworksCode1
Seeing Through Clouds in Satellite ImagesCode1
SSSNET: Semi-Supervised Signed Network ClusteringCode1
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusionCode1
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local FusionCode1
Attentive Contextual Attention for Cloud RemovalCode1
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal—0
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images—0
Cloud Removal from Satellite Images—0
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models—0
Cloud removal Using Atmosphere Model—0
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks—0
Cloud Removal With PolSAR-Optical Data Fusion Using A Two-Flow Residual Network—0
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"—0
Cross-Frequency Implicit Neural Representation with Self-Evolving Parameters—0
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery—0
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets—0
Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration—0
MIMRS: A Survey on Masked Image Modeling in Remote Sensing—0
MM811 Project Report: Cloud Detection and Removal in Satellite Images—0
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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