Training Supervised Neural Networks for PolSAR Despeckling With an Hybrid Approach-Reference-Cited by-同舟云学术

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Training Supervised Neural Networks for PolSAR Despeckling With an Hybrid Approach

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Lu Xialei¹,Vitale Sergio¹^ORCID,Aghababei Hossein²^ORCID,Ferraioli Giampaolo³^ORCID,Pascazio Vito¹^ORCID

Affiliation:

1. Department of Engineering, University Parthenope of Naples, Naples, Italy

2. Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands

3. National Inter-University Consortium for Telecommunications, Parma, Italy

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10319732.pdf?arnumber=10319732

Reference15 articles.

1. Polarimetric sar speckle noise model

2. Some fundamental properties of speckle*

3. A new technique for noise filtering of SAR interferometric phase images

4. Refined filtering of interferometric phase from INSAR data

5. NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

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. PolSAR Classification Assessment for Deep Learning Despeckling Filter;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

2. Sentinel-1 Dual-Polarization SAR Images Despeckling Network Based on Unsupervised Learning;IEEE Transactions on Geoscience and Remote Sensing;2024

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