A Novel Iterative Semi-Supervised Learning Framework based on Few-shot Samples for China Coastal Wetland Land Cover Classification Using GF-5 Hyperspectral Imagery
Author:
Affiliation:
1. Hohai University,School of Earth Sciences and Engineering,China
2. University of Extremadura,Dept. of Technology of Computers and Communications,Spain
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10640349/10640352/10640973.pdf?arnumber=10640973
Reference14 articles.
1. Probabilistic Collaborative Representation Based Ensemble Learning for Classification of Wetland Hyperspectral Imagery
2. Hyperspectral and Multispectral Classification for Coastal Wetland Using Depthwise Feature Interaction Network
3. SemiBoost: Boosting for Semi-Supervised Learning
4. Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks
5. Dual Graph Convolutional Network for Hyperspectral Image Classification With Limited Training Samples
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