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 with no code
Diffusion Models for Earth Observation Use-cases: from cloud removal to urban change detection
The advancements in the state of the art of generative Artificial Intelligence (AI) brought by diffusion models can be highly beneficial in novel contexts involving Earth observation data.
Cloud Removal in Remote Sensing Using Sequential-Based Diffusion Models
In particular, MmDMs is a novel diffusion model that reconstructs the reverse process of denosing diffusion probabilistic models (DDPMs) to integrate additional information from auxiliary modalities (e. g., synthetic aperture radar robust to the corruption of clouds) to help the distribution learning of main modality (i. e., optical satellite imagery).
On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites
The amount of data generated by Earth observation satellites can be enormous, which poses a great challenge to the satellite-to-ground connections with limited rate.
MM811 Project Report: Cloud Detection and Removal in Satellite Images
The outcome of this project can be used to develop applications that require cloud-free satellite images.
Cloud removal Using Atmosphere Model
Cloud removal is an essential task in remote sensing data analysis.
Attention mechanism-based generative adversarial networks for cloud removal in Landsat images
First, attention maps of the input cloudy images are generated to extract the cloud distributions and features through an attentive recurrent network.
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline
The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning.
Cloud Removal from Satellite Images
In this report, we have analyzed available cloud detection technique using sentinel hub.
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal
In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of application.
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.
