MSDF-Net: A Multi-Scale Deep Fusion Network with Dilated Convolutions for Cloud Removal from Sentinel-2 Imagery-Reference-Cited by-同舟云学术

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MSDF-Net: A Multi-Scale Deep Fusion Network with Dilated Convolutions for Cloud Removal from Sentinel-2 Imagery

Published:2023-10-31 Issue: Volume: Page:
ISSN:
Container-title:2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
language:
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Author:

Jayakrishnan A¹,Venkatesan M¹,Prabhavathy P²,Alkha Mohan³

Affiliation:

1. National Institute of Technology Puducherry,Department of Computer Science and Engineering,Puducherry,India,609609

2. Vellore Institute of Technology,School of Information Technology,Vellore,Tamil Nadu,India,632014

3. Indian Institute of Information Technology Tiruchirappalli,Department of Computer Science and Engineering,Tiruchirappalli,Tamil Nadu,India,620012

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10317071/10317095/10317471.pdf?arnumber=10317471

Reference37 articles.

1. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery

2. Cloud Removal in Optical Remote Sensing Imagery Using Multiscale Distortion-Aware Networks

3. A Bandelet-Based Inpainting Technique for Clouds Removal From Remotely Sensed Images

4. Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery

5. Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. NB_Re³: A Novel Framework for Reconstructing High-Quality Reflectance Time Series Taking Full Advantage of High-Quality NDVI and Multispectral Autocorrelations;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

2. CERMF-Net: A SAR-Optical Feature Fusion for Cloud Elimination From Sentinel-2 Imagery Using Residual Multiscale Dilated Network;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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