Key sub-region feature fusion network for fine-grained ship detection and recognition in remote sensing images-Reference-Cited by-同舟云学术

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Key sub-region feature fusion network for fine-grained ship detection and recognition in remote sensing images

Published:2023 Issue:9 Volume:28 Page:2940-2955
ISSN:1006-8961
Container-title:Journal of Image and Graphics
language:en
Short-container-title:

Author:

Lei Zhang, ,Wen Chen,Yuehuan Wang

Publisher

Aerospace Information Research Institute, Chinese Academy of Sciences

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Human-Computer Interaction

Reference30 articles.

1. Burges C,Shaked T,Renshaw E,Lazier A,Deeds M,Hamilton N and Hullender G. 2005. Learning to rank using gradient descent//Proceedings of the 22nd International Conference on Machine Learning. Bonn, Germany:ACM:89-96[DOI:10.1145/1102351. 1102363]

2. Cui Y,Jia M L,Lin T Y,Song Y and Belongie S. 2019. Class-balanced loss based on effective number of samples//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach,USA:IEEE:9260-9269[DOI:10. 1109/CVPR.2019.00949]

3. Ding J,Xue N,Long Y,Xia G S and Lu Q K. 2019. Learning RoI Transformer for oriented object detection in aerial images//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach,USA:IEEE:2844-2853[DOI:10.1109/CVPR.2019.00296]

4. Feng Y C,Diao W H,Sun X,Yan M L and Gao X. 2019. Towards automated ship detection and category recognition from high-resolution aerial images. Remote Sensing,11(16):#1901[DOI:10.3390/rs11161901]

5. Gallego A J,Pertusa A and Gil P. 2018. Automatic ship classification from optical aerial images with convolutional neural networks.Remote Sensing,10(4):#511[DOI:10.3390/rs10040511]

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