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
Most implemented papers
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.
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.
A Remote Sensing Image Dataset for Cloud Removal
Removing clouds is an indispensable pre-processing step in remote sensing image analysis.
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.
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.
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i. e., the data usability is greatly reduced.
SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS
bermudezjose/SAR2Optical-using-cGANS
•
•
ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2018
In this work, we combine the fact that SAR images are hardly affected by clouds with the ability of cGANS for image translation in order to map optical images from SAR ones so as to recover regions that are covered by clouds.
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.
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.
Seeing Through Clouds in Satellite Images
This paper presents a neural-network-based solution to recover pixels occluded by clouds in satellite images.
