Addressing Sample Inconsistency for Semisupervised Object Detection in Remote Sensing Images-Reference-Cited by-同舟云学术

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Addressing Sample Inconsistency for Semisupervised Object Detection in Remote Sensing Images

Published:2024 Issue: Volume:17 Page:6933-6944
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wang Yuhao¹^ORCID,Yao Lifan²^ORCID,Meng Gang³^ORCID,Zhang Xinye²^ORCID,Song Jiayun²^ORCID,Zhang Haopeng¹^ORCID

Affiliation:

1. Department of Aerospace Information Engineering (Image Processing Center), School of Astronautics, Beihang University, Beijing, China

2. Qingdao Research Institute of Beihang University, Shandong, China

3. Beijing Institute of Remote Sensing Information, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10463140.pdf?arnumber=10463140

Reference41 articles.

1. X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data

2. DIOD: Fast and Efficient Weakly Semi-Supervised Deep Complex ISAR Object Detection

3. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks

4. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;Tarvainen;Adv. Neural Inf. Process. Syst.,2017

5. Active Teacher for Semi-Supervised Object Detection

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