Cloud Removal
22 papers with code • 2 benchmarks • 3 datasets
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
Benchmarks
These leaderboards are used to track progress in Cloud Removal
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
SEN12MS-CR
RICE
SEN12MS-CR-TS
Latest papers
SSSNET: Semi-Supervised Signed Network Clustering
sherylhyx/sssnet_signed_clustering
•
•
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
VSainteuf/utae-paps
•
•
ICCV 2021
We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations.
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Sebbyraft/Spatio-Temporal_SAR-Optical_Data_Fusion_for_Cloud_Removal
•
•
Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
Seeing Through Clouds in Satellite Images
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images.
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
Akash-Ramjyothi/Cloud-Removal-with-GAN-Satellite-Image-Processing
•
•
We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images.
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
Penn000/SpA-GAN_for_cloud_removal
•
•
Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties.
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
This work has been accepted by IEEE TGRS for publication.
Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion
Optical remote sensing imagery is at the core of many Earth observation activities.
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds.
A Remote Sensing Image Dataset for Cloud Removal
Removing clouds is an indispensable pre-processing step in remote sensing image analysis.
