Ship Recognition for Complex SAR Images via Dual-Branch Transformer Fusion Network-Reference-Cited by-同舟云学术

Ship Recognition for Complex SAR Images via Dual-Branch Transformer Fusion Network

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:

Sun Zhongzhen¹^ORCID,Leng Xiangguang¹^ORCID,Zhang Xianghui¹^ORCID,Xiong Boli¹,Ji Kefeng¹^ORCID,Kuang Gangyao¹

Affiliation:

1. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Independent Research Fund of Key Laboratory of Satellite Information Intelligent Processing and Application Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10522760.pdf?arnumber=10522760

Reference16 articles.

1. Ship Detection From Raw SAR Echo Data

2. ShipGeoNet: SAR Image-Based Geometric Feature Extraction of Ships Using Convolutional Neural Networks

3. BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

4. YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI

5. An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

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