FIAD net: a Fast SAR ship detection network based on feature integration attention and self-supervised learning-Reference-Cited by-同舟云学术

FIAD net: a Fast SAR ship detection network based on feature integration attention and self-supervised learning

Published:2022-02-16 Issue:4 Volume:43 Page:1485-1513
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Wang Deyi¹,Zhang Chengkun²,Han Min³

Affiliation:

1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China

2. Department of Computer Technology and Application, Qinghai University, Xining, Qinghai, China

3. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology, Dalian, China

Funder

National Natural Science Foundation of China

Fundamental Research Fund for the Central Universities

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2022.2042617

Reference52 articles.

1. Ship detection performance using simulated dual-polarization RADARSAT constellation mission data

2. Bochkovskiy, A., C.Y. Wang, and H.Y. Mark Liao. 2020. “Yolov4: Optimal Speed and Accuracy of Object Detection.” CoRR abs/2004.10934, https://arxiv.org/abs/2004.10934

3. Detection of Small Ship Objects Using Anchor Boxes Cluster and Feature Pyramid Network Model for SAR Imagery

4. Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities

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