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Cloud Removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Seeing Through Clouds in Satellite Images
Cloud removal in remote sensing images using generative adversarial networks and SAR-to-optical image translation
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
A Remote Sensing Image Dataset for Cloud Removal
Reading Industrial Inspection Sheets by Inferring Visual Relations
A Conditional Generative Adversarial Network to Fuse Sar And Multispectral Optical Data For Cloud Removal From Sentinel-2 Images
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation
Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network
Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.