ST-Net: Scattering Topology Network for Aircraft Classification in High-Resolution SAR Images-Reference-Cited by-同舟云学术

ST-Net: Scattering Topology Network for Aircraft Classification in High-Resolution SAR Images

Published:2023 Issue: Volume:61 Page:1-17
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Kang Yuzhuo¹^ORCID,Wang Zhirui¹^ORCID,Zuo Haoyu²,Zhang Yidan¹^ORCID,Yang Zhujun¹^ORCID,Sun Xian¹^ORCID,Fu Kun¹^ORCID

Affiliation:

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

2. Dyson School of Design Engineering, Imperial College London, South Kensington, London, U.K

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10024113.pdf?arnumber=10024113

Reference70 articles.

1. Improving SAR Automatic Target Recognition Models With Transfer Learning From Simulated Data

2. A Discriminative Feature Learning Approach for Deep Face Recognition

3. Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data

4. SA-Net: Shuffle Attention for Deep Convolutional Neural Networks

5. SCAN: Scattering Characteristics Analysis Network for Few-Shot Aircraft Classification in High-Resolution SAR Images

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