SAR Image Recognition Using ViT Network and Contrastive Learning Framework With Unlabeled Samples-Reference-Cited by-同舟云学术

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SAR Image Recognition Using ViT Network and Contrastive Learning Framework With Unlabeled Samples

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Deng Jianping¹,Zhu Yuying²^ORCID,Zhang Shuning³^ORCID,Chen Si¹^ORCID

Affiliation:

1. School of Electronic and Information Engineering, Suzhou Vocational University, Suzhou, China

2. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China

3. School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10316295.pdf?arnumber=10316295

Reference19 articles.

1. Discriminating Ship From Radio Frequency Interference Based on Noncircularity and Non-Gaussianity in Sentinel-1 SAR Imagery

2. Variable Length Sequential Iterable Convolutional Recurrent Network for UWB-IR Vehicle Target Recognition

3. Ship Detection From Raw SAR Echo Data

4. Spatial Singularity-Exponent-Domain Multiresolution Imaging-Based SAR Ship Target Detection Method

5. FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features

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