Semi-Supervised Classification of Dual-Frequency PolSAR Image Using Joint Feature Learning and Cross Label-Information Network-Reference-Cited by-同舟云学术

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Semi-Supervised Classification of Dual-Frequency PolSAR Image Using Joint Feature Learning and Cross Label-Information Network

Published:2022 Issue: Volume:60 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Xin Xinyue¹,Li Ming¹^ORCID,Wu Yan²^ORCID,Zheng Mingjie³,Zhang Peng¹^ORCID,Xu Dazhi¹,Wang Jili³

Affiliation:

1. National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

2. Remote Sensing Image Processing and Fusion Group, School of Electronics Engineering, Xidian University, Xi’an, China

3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

Natural Science Foundation of China

Civil Space Thirteen Five Years Pre-Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09906133.pdf?arnumber=9906133

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4. SemiPSCN: Polarization Semantic Constraint Network for Semi-Supervised Segmentation in Large-Scale and Complex-Valued PolSAR Images;IEEE Transactions on Geoscience and Remote Sensing;2024

5. PolSAR-MPIformer: A Vision Transformer Based on Mixed Patch Interaction for Dual-Frequency PolSAR Image Adaptive Fusion Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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