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.

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Papers

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Showing 41–50 of 56 papers

TitleStatusHype
MM811 Project Report: Cloud Detection and Removal in Satellite Images—0
Cloud removal Using Atmosphere Model—0
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
Cloud Removal from Satellite Images—0
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal—0
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization—0
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks—0
A Remote Sensing Image Dataset for Cloud RemovalCode0
Reading Industrial Inspection Sheets by Inferring Visual Relations—0
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