{"title":"混合推理网络与面向类别的分层表示法,用于少镜头合成孔径雷达目标识别","authors":"Haohao Ren;Sen Liu;Lei Miao;Xuelian Yu;Lin Zou;Yun Zhou;Hao Tang","doi":"10.1109/JSEN.2024.3421997","DOIUrl":null,"url":null,"abstract":"The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. The existing SAR ATR methods can achieve impressive results with the great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR target recognition under the condition of sample scarcity is a fundamental issue to be overcome urgently. In this article, we put forward an ATR method named hybrid reasoning network with class-oriented hierarchical representation (HRNCHR) to achieve SAR target recognition with limited training samples. First, we develop a feature extraction model with local–global information concurrent refinement mechanism (LGICRM), which aims to simultaneously refine diverse features at both local and global levels from limited samples. Then, a hierarchical representation space that can learn hierarchical relationships between categories by utilizing diverse features learned from the feature extraction model is established, which is convenient to generalize to few-shot SAR target recognition tasks with the aid of category hierarchical relationship. Finally, a hybrid reasoning strategy is presented, which affords to fuse the reasoning results of instance level and prototype level to promote the accuracy of a decision-making system. Extensive evaluation experiments on the publicly released moving and stationary target acquisition and recognition (MSTAR) dataset and OpenSARship dataset illustrate that the proposed method surpasses many state-of-the-art SAR ATR methods.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition\",\"authors\":\"Haohao Ren;Sen Liu;Lei Miao;Xuelian Yu;Lin Zou;Yun Zhou;Hao Tang\",\"doi\":\"10.1109/JSEN.2024.3421997\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. The existing SAR ATR methods can achieve impressive results with the great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR target recognition under the condition of sample scarcity is a fundamental issue to be overcome urgently. In this article, we put forward an ATR method named hybrid reasoning network with class-oriented hierarchical representation (HRNCHR) to achieve SAR target recognition with limited training samples. First, we develop a feature extraction model with local–global information concurrent refinement mechanism (LGICRM), which aims to simultaneously refine diverse features at both local and global levels from limited samples. Then, a hierarchical representation space that can learn hierarchical relationships between categories by utilizing diverse features learned from the feature extraction model is established, which is convenient to generalize to few-shot SAR target recognition tasks with the aid of category hierarchical relationship. Finally, a hybrid reasoning strategy is presented, which affords to fuse the reasoning results of instance level and prototype level to promote the accuracy of a decision-making system. 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引用次数: 0
摘要
深度学习的兴起有力地推动了合成孔径雷达(SAR)图像中目标自动识别(ATR)技术的快速发展。现有的合成孔径雷达自动目标识别(ATR)方法可以利用大量的标注样本获得令人印象深刻的结果。然而,在实际合成孔径雷达应用场景中,获取大量合成孔径雷达样本的成本很高,有时甚至不可行。因此,样本稀缺条件下的合成孔径雷达目标识别是亟待解决的基本问题。本文提出了一种名为 "面向类分层表示的混合推理网络(HRNCHR)"的 ATR 方法,以实现有限训练样本下的合成孔径雷达目标识别。首先,我们开发了一种具有局部-全局信息并发提炼机制(LGICRM)的特征提取模型,旨在从有限的样本中同时提炼局部和全局层面的各种特征。然后,我们建立了一个分层表示空间,利用从特征提取模型中学习到的各种特征来学习类别之间的分层关系,从而方便地借助类别分层关系将其推广到少镜头 SAR 目标识别任务中。最后,提出了一种混合推理策略,它可以融合实例级和原型级的推理结果,从而提高决策系统的准确性。在公开发布的移动和静止目标获取与识别(MSTAR)数据集和 OpenSARship 数据集上进行的大量评估实验表明,所提出的方法超越了许多最先进的 SAR ATR 方法。
Hybrid Reasoning Network With Class-Oriented Hierarchical Representation for Few-Shot SAR Target Recognition
The rise of deep learning has furnished a potent boost for the rapid development of automatic target recognition (ATR) in synthetic aperture radar (SAR) imagery. The existing SAR ATR methods can achieve impressive results with the great many labeled samples available. However, in real SAR application scenarios, the acquisition of quite a few SAR samples is costly or sometimes infeasible. Thus, SAR target recognition under the condition of sample scarcity is a fundamental issue to be overcome urgently. In this article, we put forward an ATR method named hybrid reasoning network with class-oriented hierarchical representation (HRNCHR) to achieve SAR target recognition with limited training samples. First, we develop a feature extraction model with local–global information concurrent refinement mechanism (LGICRM), which aims to simultaneously refine diverse features at both local and global levels from limited samples. Then, a hierarchical representation space that can learn hierarchical relationships between categories by utilizing diverse features learned from the feature extraction model is established, which is convenient to generalize to few-shot SAR target recognition tasks with the aid of category hierarchical relationship. Finally, a hybrid reasoning strategy is presented, which affords to fuse the reasoning results of instance level and prototype level to promote the accuracy of a decision-making system. Extensive evaluation experiments on the publicly released moving and stationary target acquisition and recognition (MSTAR) dataset and OpenSARship dataset illustrate that the proposed method surpasses many state-of-the-art SAR ATR methods.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
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