Iterative Semi-Supervised Learning With Few-Shot Samples for Coastal Wetland Land Cover Classification-Reference-Cited by-同舟云学术

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Iterative Semi-Supervised Learning With Few-Shot Samples for Coastal Wetland Land Cover Classification

Published:2024 Issue: Volume:62 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:

Su Hongjun¹^ORCID,Lu Hongliang²^ORCID,Zheng Pan²^ORCID,Zheng Hengyi²^ORCID,Xue Zhaohui¹^ORCID,Du Qian³^ORCID

Affiliation:

1. College of Geography and Remote Sensing, Hohai University, Nanjing, China

2. School of Earth Sciences and Engineering, Hohai University, Nanjing, China

3. Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS, USA

Funder

National Natural Science Foundation of China

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/36/10354519/10659874.pdf?arnumber=10659874

Reference41 articles.

1. Multi-Layer Global Spectral-Spatial Attention Network for Wetland Hyperspectral Image Classification

2. Wetland mapping based on feature optimization and Google Earth Engine in Zhalong Nature Reserve

3. Probabilistic Collaborative Representation Based Ensemble Learning for Classification of Wetland Hyperspectral Imagery

4. Multisensor approach to determine changes of wetland characteristics in semiarid environments (central Spain)

5. A Probabilistic Sample Boosting Approach With Adaptive Representation Coefficient Consistency for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery

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