Confidence-Weighted Dual-Teacher Networks With Biased Contrastive Learning for Semi-Supervised Semantic Segmentation in Remote Sensing Images-Reference-Cited by-同舟云学术

Confidence-Weighted Dual-Teacher Networks With Biased Contrastive Learning for Semi-Supervised Semantic Segmentation in Remote Sensing Images

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:

Xin Yi¹^ORCID,Fan Zide²^ORCID,Qi Xiyu¹^ORCID,Zhang Yidan¹^ORCID,Li Xinming²

Affiliation:

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

2. Aerospace Information Research Institute and the Key Laboratory of Network Information System Technology (NIST), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

Funder

Strategic Priority Research Program of Chinese Academy of Sciences

Future Star of Aerospace Information Research Institute, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10466751.pdf?arnumber=10466751

Reference56 articles.

1. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

2. Classification with an edge: Improving semantic image segmentation with boundary detection

3. Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification

4. Oriented objects as pairs of middle lines

5. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;Tarvainen

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