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Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images

文献类型：期刊论文

作者Zhang, Shuangqing1; Zhang, Zhang2; Li, Da2; Jia, Zhen2; Li, Chenglong1; Wang, Liang2; Zhao, Hanyu2; Wang, Duo3
刊名IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
出版日期2024
卷号21页码:5
关键词Generalized few-shot object detection (G-FSOD) remote sensing images (RSIs) ship detection Generalized few-shot object detection (G-FSOD) remote sensing images (RSIs) ship detection
ISSN号1545-598X
DOI10.1109/LGRS.2024.3432280
通讯作者Li, Chenglong(lcl1314@foxmail.com)
英文摘要Fine-grained ship detection in remote sensing images (RSIs) depends heavily on numerous training data with expensive manual annotations. Learning novel ship categories from very few labeled samples and without forgetting the learned knowledge of seen categories is important to real-world applications. In this letter, we formulate fine-grained ship detection in RSIs as a problem of generalized few-shot object detection (G-FSOD). Existing methods often neglect the structured information in ship taxonomy, and thus result in mutually exclusive representations between base and novel classes and hinder the transfer of the learned knowledge to the novel concepts under the few-shot settings. To handle this problem, we propose to incorporate the inherent hierarchical taxonomy in ship classes into the generalized few-shot ship detection to leverage the shared knowledge among base and novel classes. In particular, a ship detector is trained based on the coarsest class labels and a multitask classification network is built to distinguish various ships at both coarse and fine-grained levels on base classes, which leads to a generalized ship representation between base classes to novel classes. To build the classifier of novel classes, a prototype bank is constructed with the few-shot samples of novel classes, without the wreck of the feature extractor so as to maintain the performance on base classes. Extensive experiments on two large-scale ship detection datasets demonstrate the effectiveness of our method against state-of-the-art methods.
WOS关键词DATASET
资助项目Joint Funds of the National Natural Science Foundation of China[U20B2068] ; National Science and Technology Major Project[2022ZD0117901] ; National Natural Science Foundation of China[62376004] ; National Natural Science Foundation of China[62373355] ; National Natural Science Foundation of China[62106260] ; Natural Science Foundation of Anhui Province[2208085J18]
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001283693700020
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Joint Funds of the National Natural Science Foundation of China ; National Science and Technology Major Project ; National Natural Science Foundation of China ; Natural Science Foundation of Anhui Province
源URL[http://ir.ia.ac.cn/handle/173211/59294]  
专题多模态人工智能系统全国重点实验室
通讯作者Li, Chenglong
作者单位1.Anhui Univ, Informat Mat & Intelligent Sensing Lab Anhui Prov, Anhui Prov Key Lab Multimodal Cognit Computat, Hefei 230601, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
3.Beijing Inst Control Engn, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Shuangqing,Zhang, Zhang,Li, Da,et al. Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2024,21:5.
APA Zhang, Shuangqing.,Zhang, Zhang.,Li, Da.,Jia, Zhen.,Li, Chenglong.,...&Wang, Duo.(2024).Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,21,5.
MLA Zhang, Shuangqing,et al."Class Hierarchy-Guided Generalized Few-Shot Ship Detection in Remote Sensing Images".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21(2024):5.

入库方式： OAI收割

来源：自动化研究所

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