A coarse-to-fine vehicle detection in large SAR scenes based on GL-CFAR and PRID R-CNN-Reference-Cited by-同舟云学术

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A coarse-to-fine vehicle detection in large SAR scenes based on GL-CFAR and PRID R-CNN

Published:2023-04-18 Issue:8 Volume:44 Page:2518-2547
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Fan Yunfeng¹²,Tian Sijing¹,Sheng Qinghong¹,Li Jun¹,Chen Jianqiang¹,Wang Bo¹,Ma Jianguo³

Affiliation:

1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, China

2. China Aerospace Science and Technology Corporation, Shanghai Electro-Mechanical Engineering Institute, Shanghai, China

3. University of Maryland, Maryland, College Park, USA

Funder

the National Key Laboratory Foundation

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2023.2203341

Reference60 articles.

1. Bochkovskiy, A., C.Y. Wang, and H.Y. Mark Liao. 2020. “Yolov4: Optimal Speed and Accuracy of Object Detection.

2. Cascade R-CNN: Delving Into High Quality Object Detection

3. A survey on object detection in optical remote sensing images

4. A Practical Kernel Criterion for Feature Extraction and Recognition of MSTAR SAR Images

5. Target Classification Using the Deep Convolutional Networks for SAR Images

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1. Enhanced-YOLOv8: A new small target detection model;Digital Signal Processing;2024-10

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