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.
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Benchmarks
These leaderboards are used to track progress in Cloud Removal
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SEN12MS-CR
RICE
SEN12MS-CR-TS
Latest papers
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
boranhan/geospatial_foundation_models
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A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing representations in this field.
IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images
songyxing/idf-cr
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IDF-CR consists of a pixel space cloud removal module (Pixel-CR) and a latent space iterative noise diffusion network (IND).
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.
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.
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.
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.
PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-performance Cloud Removal from Multi-temporal Satellite Imagery
Satellite imagery analysis plays a pivotal role in remote sensing; however, information loss due to cloud cover significantly impedes its application.
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.
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.
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.
