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 |
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SEN12MS-CR
RICE
SEN12MS-CR-TS
Most implemented papers
Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
Sebbyraft/Spatio-Temporal_SAR-Optical_Data_Fusion_for_Cloud_Removal
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Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study.
Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
VSainteuf/utae-paps
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ICCV 2021
We also introduce PASTIS, the first open-access SITS dataset with panoptic annotations.
SSSNET: Semi-Supervised Signed Network Clustering
sherylhyx/sssnet_signed_clustering
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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.
SEN12MS-CR-TS: A Remote Sensing Data Set for Multi-modal Multi-temporal Cloud Removal
About half of all optical observations collected via spaceborne satellites are affected by haze or clouds.
GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion
xufangchn/glf-cr
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The challenge of the cloud removal task can be alleviated with the aid of Synthetic Aperture Radar (SAR) images that can penetrate cloud cover.
High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark
In this paper, we introduce Planet-CR, a benchmark dataset for high-resolution cloud removal with multi-modal and multi-resolution data fusion.
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
Clouds and haze often occlude optical satellite images, hindering continuous, dense monitoring of the Earth's surface.
U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
prs-eth/u-tilise
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Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages.
DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
Optical satellite images are a critical data source; however, cloud cover often compromises their quality, hindering image applications and analysis.
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
littlebeen/Diffusion-Enhancement-for-CR
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The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images.
