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 with no code
Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks
The approach can be roughly divided into two steps: in the first step, a specially designed convolutional neural network (CNN) translates the synthetic aperture radar (SAR) images into simulated optical images in an object-to-object manner; in the second step, the simulated optical image, together with the SAR image and the optical image corrupted by clouds, is fused to reconstruct the corrupted area by a generative adversarial network (GAN) with a particular loss function.
Reading Industrial Inspection Sheets by Inferring Visual Relations
Over the years, millions of such inspection sheets have been recorded and the data within these sheets has remained inaccessible.
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images
In this paper, we present the first conditional generative adversarial network (cGAN) architecture that is specifically designed to fuse synthetic aperture radar (SAR) and optical multi-spectral (MS) image data to generate cloud- and haze-free MS optical data from a cloud-corrupted MS input and an auxiliary SAR image.
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation
In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods.
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs.
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Remote sensing images often suffer from cloud cover.
