Target Representation and Classification with Limited Data in Synthetic Aperture Radar Images

机译：合成孔径雷达图像中有限数据的目标表示和分类

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Recently, with the introduction and development of deep learning based detection and classification methods, various applications in optical images have been put into practice. However, few automatic target recognition (ATR) approaches in Synthetic Aperture Radar (SAR) images can be used practically. Two reasons underlying it are the complicated imaging mechanism of SAR and limited sample data for optimizing the models. This paper focus on target representation and classification with limited data based on zero-shot learning (ZSL) and fewshot learning (FSL), and provides a comprehensive investigation of existing ZSL/FSL algorithms.

机译：近来，随着基于深度学习的检测和分类方法的引入和发展，光学图像中的各种应用已被付诸实践。但是，在合成孔径雷达（SAR）图像中几乎没有自动目标识别（ATR）方法可以实际使用。其背后的两个原因是SAR的成像机制复杂，以及用于优化模型的有限样本数据。本文着重研究基于零击学习（ZSL）和少击学习（FSL）的有限数据的目标表示和分类，并对现有ZSL / FSL算法进行了全面的研究。

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《General Assembly and Scientific Symposium of the International Union of Radio Science》|2020年|1-4|共4页
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Qian Song; Feng Xu; Haipeng Wang;
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1. Synthetic aperture radar target classification via joint sparse representation of multi-level dominant scattering images [J] . Jin Lizhong, Chen Junjie, Peng Xinguang Optik: Zeitschrift fur Licht- und Elektronenoptik: = Journal for Light-and Electronoptic . 2019,第期

机译：合成孔径雷达目标分类通过联合稀疏表示的多级优势散射图像
2. Combining pyramid representation and AdaBoost for urban scene classification using high-resolution synthetic aperture radar images [J] . Radar, Sonar & Navigation, IET . 2011,第1期

机译：使用高分辨率合成孔径雷达图像将金字塔表示法和AdaBoost结合用于城市场景分类
3. Target Recognition of Synthetic Aperture Radar Images Based on Two-Phase Sparse Representation [J] . Wen Li, Jun Yang, Yide Ma Journal of Sensors . 2020,第1期

机译：基于两相稀疏表示的合成孔径雷达图像的目标识别
4. Blending synthetic and measured data using transfer learning for synthetic aperture radar target classification [C] . Julia M. Arnold, Linda J. Moore, Edmund G. Zelnio SPIE Defense + Security Conference . 2018

机译：使用转移学习混合合成数据和测量数据，以进行合成孔径雷达目标分类
5. The exploitation of multi-look synthetic aperture radar and inverse synthetic aperture radar images for non-cooperative target recognition [D] . Papson, Scott 2007

机译：多视角合成孔径雷达和逆合成孔径雷达图像的非合作目标识别开发
6. Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification [O] . Xinzheng Zhang, Yijian Wang, Zhiying Tan, 2017

机译：合成孔径雷达（SAR）目标图像分类的两阶段多任务表示学习
7. Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification [O] . Xinzheng Zhang, Yijian Wang, Zhiying Tan, 2017

机译：合成孔径雷达（saR）目标图像分类的两阶段多任务表示学习
8. Target Classification Using SAR (Synthetic Aperture Radar) Polarimetric Data. [R] . Callaghan, A. P. 1989

机译：利用saR（合成孔径雷达）极化数据进行目标分类。

Target Representation and Classification with Limited Data in Synthetic Aperture Radar Images

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