An attention cut classification network for fine-grained ship classification in remote sensing images-Reference-Cited by-同舟云学术

An attention cut classification network for fine-grained ship classification in remote sensing images

Published:2022-02-13 Issue:4 Volume:13 Page:418-427
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Song Yixuan¹²,Song Fei¹²³^ORCID,Jin Lei¹²,Lei Tao¹²,Liu Gang⁴,Jiang Ping¹²,Peng Zhenming³

Affiliation:

1. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu, China

2. School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China

3. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

4. Shanghai Institute of Satellite Engineering, Shanghai, China

Funder

National Natural Science Foundation of China

Open Research Fund of Key Optical Engineering, Chinese Academy of Sciences

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2022.2035464

Reference19 articles.

1. Ship detection and recognitionin high-resolution satellite images

2. Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks

3. Destruction and Construction Learning for Fine-Grained Image Recognition

4. Chen, K., W. Ming, J. Liu, and C. Zhang. 2020. “FGSD: A Dataset for Fine-Grained Ship Detection in High Resolution Satellite Images.” CoRR abs/2003.06832. https://arxiv.org/abs/2003.06832.

5. Deep Residual Learning for Image Recognition

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