Papers with Code 아카이브
Cloud Removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 2
- 보존된 평가 기록
- 17
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CUG-BEODL/MS2TAN
공식 코드https://github.com/PatrickTUM/SEN12MS-CR-TS
공식 코드Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
https://github.com/Penn000/SpA-GAN_for_cloud_removal
공식 코드Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
https://github.com/Sebbyraft/Spatio-Temporal_SAR-Optical_Data_Fusion_for_Cloud_Removal
공식 코드Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model
https://github.com/VSainteuf/utae-paps
공식 코드Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks
https://github.com/WHUQZhang/STS-CNN
공식 코드https://github.com/boranhan/geospatial_foundation_models
공식 코드Bridging Remote Sensors with Multisensor Geospatial Foundation Models
https://github.com/deng-ai-lab/swformer
공식 코드https://github.com/huangwenwenlili/aca-crnet
공식 코드https://github.com/littlebeen/Diffusion-Enhancement-for-CR
공식 코드Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing Imagery
https://github.com/ly403/emrdm
공식 코드https://github.com/prs-eth/u-tilise
공식 코드U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
https://github.com/sherylhyx/sssnet_signed_clustering
공식 코드https://github.com/songyxing/idf-cr
공식 코드IDF-CR: Iterative Diffusion Process for Divide-and-Conquer Cloud Removal in Remote-sensing Images
https://github.com/xavierjiezou/diffcr
공식 코드DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
https://github.com/xavierjiezou/pmaa
공식 코드https://github.com/xufangchn/glf-cr
공식 코드https://github.com/zhou-hangyu/allclear
공식 코드AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
https://github.com/zhu-xlab/planet-cr
공식 코드https://github.com/Akash-Ramjyothi/Cloud-Removal-with-GAN-Satellite-Image-Processing
https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-System-using-Generative-Adversarial-Networks-Satellite-Image-Processing
https://github.com/Akash-Ramjyothi/Cloud-and-Fog-removal-from-Satellite-Images-using-Generative-Adversarial-Netrowks-GANs-
https://github.com/PatrickTUM/UnCRtainTS
UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series
https://github.com/VSAnimator/stgan
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
https://github.com/XavierJiezou/cloud-removal-deploy
https://github.com/ameraner/dsen2-cr
https://github.com/bermudezjose/SAR2Optical-using-cGANS
https://github.com/chouhan-avinash/DGMR
https://github.com/come880412/CTGAN
Cloud Removal in Satellite Images Using Spatiotemporal Generative Networks
https://github.com/microsoft/farmvibes-ai
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.