Hybrid Conv-Attention Networks for Synthetic Aperture Radar Imagery-Based Target Recognition-Reference-Cited by-同舟云学术

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Hybrid Conv-Attention Networks for Synthetic Aperture Radar Imagery-Based Target Recognition

Published:2024 Issue: Volume:12 Page:53045-53055
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Yoon Jiseok¹^ORCID,Song Jeongheon²,Hussain Tanveer³,Khowaja Sunder Ali⁴^ORCID,Muhammad Khan⁵^ORCID,Lee Ik Hyun¹^ORCID

Affiliation:

1. IKLAB Inc., Siheung-Si, South Korea

2. Korea Aerospace Research Institute, Daejeon, South Korea

3. Department of Computer Science, Edge Hill University, Ormskirk, U.K

4. Department of Telecommunication Engineering, University of Sindh, Jamshoro, Pakistan

5. Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10496065.pdf?arnumber=10496065

Reference54 articles.

1. Synthetic aperture radar imaging;Ferro-Famil,2016

2. SAR Image Classification: A Comprehensive Study and Analysis

3. Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition;Zhang;Pattern Recognit.,2022

4. HRSID: A High-Resolution SAR Images Dataset for Ship Detection and Instance Segmentation

5. Deep Learning Meets SAR: Concepts, Models, Pitfalls, and Perspectives

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