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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 26–50 of 56 papers

TitleStatusHype
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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