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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance.

Details

Title
Few-Shot Aircraft Detection in Satellite Videos Based on Feature Scale Selection Pyramid and Proposal Contrastive Learning
Author
Zhuang, Zhou 1 ; Li, Shengyang 1   VIAFID ORCID Logo  ; Guo, Weilong 1   VIAFID ORCID Logo  ; Gu, Yanfeng 2 

 Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences, Beijing 100094, China; Key Laboratory of Space Utilization, Chinese Academy of Sciences, Beijing 100094, China; University of Chinese Academy of Sciences, Beijing 100049, China 
 School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China; Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing, Harbin 150001, China 
First page
4581
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2716606061
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.