Research Progress of Deep Learning Technology for Imaging Parameter Sensitivity of SAR Target Recognition
-
摘要: 随着人工智能技术的发展，基于深度神经网络的合成孔径雷达(SAR)目标识别得到了广泛关注。然而，SAR系统的成像机制导致了图像特性与成像参数之间的强相关性，因此深度学习框架下的目标识别算法精度极易受成像参数敏感性的干扰，这成为了制约先进智能算法部署到实际工程中的一大障碍。该文首先回顾了SAR图像目标识别技术的发展与相关数据集，从雷达工作的成像几何、载荷参数和噪声干扰3个角度，深入分析了成像参数变化对图像特性的影响；然后，从模型、数据、特征3个维度，总结归纳了现有文献关于深度学习技术对成像参数敏感性的鲁棒性与泛化性这一问题的研究进展；接下来，汇总并分析了典型方法的实验结果；最后讨论了在未来有望突破成像参数敏感性这一问题的深度学习技术研究方向。Abstract: With the development of artificial intelligence technology, Synthetic Aperture Radar (SAR) target recognition based on deep neural networks has received widespread attention. However, the imaging mechanism of SAR system leads to a strong correlation between image characteristics and imaging parameters, so the algorithm accuracy under deep learning is easily disturbed by the sensitivity of imaging parameters, which becomes a major obstacle restricting the deployment of advanced intelligent algorithms to practical engineering applications. Firstly, in this paper, the developments of SAR image target recognition technology and related data sets are reviewed, and the influence of imaging parameters on image characteristics is analyzed deeply from three aspects, i.e., imaging geometry, radar parameter and noise interference. Then, the existing literature on the robustness and generalization of deep learning technology to imaging parameter sensitivity is summarized from the three dimensions of model, data and features. Thereafter, the experimental results of typical methods are summarized and analyzed. Finally, the research direction of deep learning technology which is expected to break through the sensitivity of imaging parameters in the future is discussed.
-
表 1 SAR目标识别开源数据集
来源 数据集 目标类型 采集\仿真平台 主要成像参数特点 实测 MSTAR[7] 军用车辆 机载SAR (1) X波段，HH极化，带宽561 MHz，分辨率0.3 m
(2) 覆盖15°,17°,30°和45° 4个俯仰角，0°～360°方位角(部分严重散焦图像被剔除)Gotcha[18,19] 民用车辆 机载SAR (1) X波段，全极化，带宽640 MHz
(2) 均匀覆盖43.7°～45°中8个俯仰角，0°～360°方位角CircularSAR[20] 军用车辆 机载SAR (1) X波段，带宽 1800 MHz，分辨率0.1 m
(2) 覆盖15°,26°,31°和45° 4个俯仰角，0°～360°方位角(部分严重散焦图像被剔除)SAR-ACD[21] 民用飞机 GF3 C波段，HH极化，分辨率1 m OpenSARShip-1.0/2.0[22,23] 民用舰船 Sential-1 C波段，VV和VH极化，分辨率20～22 m FuSAR-Ship[24] 民用舰船 GF3 C波段，HH和VV极化，分辨率1.5 m 仿真 SarSIM[25] 民用车辆 CST软件 (1) X波段，HH极化，分辨率0.3 m, 3种地面环境
(2) 覆盖15°,17°,25°,30°,35°,40°和45° 7个俯仰角，0°～360°方位角(5°为间隔)SAMPLE[26] 军用车辆 XPatch软件 (1) X波段，HH极化，带宽561 MHz，分辨率0.3 m
(2) 覆盖15°～17°俯仰角，10°～80°方位角
下载: 导出CSV
表 2 优缺点及代表性方法特点总结
技术类型 优缺点 代表性参考文献 主要特点 模型端 (1) 提升融合后特征的物理可解释性
(2) 传统/物理特征的鲁棒性仍有提升空间文献[27] 将CNN模型与电磁散射特征融合 文献[28] 将CNN模型与传统几何特征融合 数据端 (1) 仅需在数据端操作，易于工程实现
(2) 性能受到扩增部分数据的质量影响文献[29] 使用仿射变化、图像旋转扩增训练集 文献[30] 使用生成对抗网络扩增训练集 文献[31] 使用电磁仿真数据扩增训练集 特征端 (1) 泛化性提升显著，存在理论基础
(2) 直推式学习限制实际应用场景文献[32] 在特征层上对齐分布 文献[33] 在特征层+像素层上对齐分布 文献[34] 在特征层+像素层+决策层上对齐分布
下载: 导出CSV
表 4 机载SAR车辆目标数据集的成像参数
参数 FARAD Ka FARAD X miniSAR 成像地点 美国科特兰空军基地 美国新墨西哥州 美国新墨西哥州 成像时间 2015.08 2015.10 2005.05 波段 Ka X Ku 中心频率(GHz) 35.6 9.6 16.8 带宽(GHz) 5 3 3 俯仰角度(°) 26～34 26～34 26～29 分辨率(m) 0.1 0.1 0.1 最大观测距离(km) 6 12 8
下载: 导出CSV
表 5 舰船检测数据集中的四种星载SAR成像参数
参数 Gaofen-3 TerraSAR-X Radarsat-2 Sentinel-1 轨道高度(km) 755 514 798 693 入射角度(°) 10～60 20～55 20～45 10～60 波段 C X C C 带宽(MHz) 240 150 100 100 分辨率(m) 0.5～100 1～16 1～100 5～20 成像范围(km) 10～650 5～100 20～50 20～400 俯仰扫描角度(°) ±20 ±25 ±11 ±20
下载: 导出CSV
表 6 SOC与EOC条件中俯仰角变化情况
俯仰角(°) 类别数量 训练数据 测试数据 SOC(17°～15°) 17 15 10 EOC(17°～30°) 17 30 3 EOC(17°～45°) 17 45 3
下载: 导出CSV
-
[1] 冯博迪, 杨海涛, 李高源, 等. 神经网络在SAR图像目标识别中的研究综述[J]. 兵器装备工程学报, 2021, 42(10): 15–22. doi: 10.11809/bqzbgcxb2021.10.003.FENG Bodi, YANG Haitao, LI Gaoyuan, et al. Research summary of convolutional neural network in SAR image target recognition[J]. Journal of Ordnance Equipment Engineering, 2021, 42(10): 15–22. doi: 10.11809/bqzbgcxb2021.10.003. [2] 黄钟泠, 姚西文, 韩军伟. 面向SAR图像解译的物理可解释深度学习技术进展与探讨[J]. 雷达学报, 2021, 11(1): 107–125. doi: 10.12000/JR21165.HUANG Zhongling, YAO Xiwen, and HAN Junwei. Progress and perspective on physically explainable deep learning for synthetic aperture radar image interpretation[J]. Journal of Radars, 2022, 11(1): 107–125. doi: 10.12000/JR21165 [3] NOVAK L M, OWIRKA G J, and NETISHEN C M. Radar target identification using spatial matched filters[J]. Pattern Recognition, 1994, 27(4): 607–617. doi: 10.1016/0031-3203(94)90040-x. [4] 董刚刚. 基于单演信号的SAR图像目标识别技术研究[D]. [博士论文], 国防科学技术大学, 2016.DONG Ganggang. Study on target recognition in SAR imagevia the monogenic signal[D]. [Ph. D. dissertation], National University of Defense Technology, 2016. [5] SISTERSON L K, DELANEY J R, GRAVINA S J, et al. An architecture for semi-automated radar image exploitation[J]. Lincoln Laboratory Journal, 1998, 11(2): 175–204. [6] MORRISON D P, ECKERT JR A C, and SHIELDS F J. Studies of advanced detection technology sensor (ADTS) data[C]. SPIE 2230, Algorithms for Synthetic Aperture Radar Imagery, Orlando, USA, 1994: 370–378. doi: 10.1117/12.177185. [7] ROSS T D, WORRELL S W, VELTEN V J, et al. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery, Orlando, USA, 1998: 566–573. doi: 10.1117/12.321859. [8] RESSLER M B, WILLIAMS R L, GROSS D C, et al. Bayesian multiple-look updating applied to the SHARP ATR system[C]. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, Orlando, USA, 2000: 418–427. doi: 10.1117/12.396354. [9] 丁军, 刘宏伟, 王英华, 等. 一种联合阴影和目标区域图像的SAR目标识别方法[J]. 电子与信息学报, 2015, 37(3): 594–600. doi: 10.11999/JEIT140713.DING Jun, LIU Hongwei, WANG Yinghua, et al. SAR target recognition by combining images of the shadow region and target region[J]. Journal of Electronics & Information Technology, 2015, 37(3): 594–600. doi: 10.11999/JEIT140713. [10] RUSSAKOVSKY O, DENG Jia, SU Hao, et al. ImageNet large scale visual recognition challenge[J]. International Journal of Computer Vision, 2015, 115(3): 211–252. doi: 10.1007/s11263-015-0816-y. [11] CHEN Sizhe, WANG Haipeng, XU Feng, et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4806–4817. doi: 10.1109/TGRS.2016.2551720. [12] 杜兰, 王兆成, 王燕, 等. 复杂场景下单通道SAR目标检测及鉴别研究进展综述[J]. 雷达学报, 2020, 9(1): 34–54. doi: 10.12000/JR19104.DU Lan, WANG Zhaocheng, WANG Yan, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes[J]. Journal of Radars, 2020, 9(1): 34–54. doi: 10.12000/JR19104. [13] 郁文贤. 自动目标识别的工程视角述评[J]. 雷达学报, 2022, 11(5): 737–752. doi: 10.12000/JR22178.YU Wenxian. Automatic target recognition from an engineering perspective[J]. Journal of Radars, 2022, 11(5): 737–752. doi: 10.12000/JR22178. [14] KECHAGIAS-STAMATIS O and AOUF N. Automatic target recognition on synthetic aperture radar imagery: A survey[J]. IEEE Aerospace and Electronic Systems Magazine, 2021, 36(3): 56–81. doi: 10.1109/MAES.2021.3049857. [15] LI Jianwei, YU Zhentao, YU Lu, et al. A comprehensive survey on SAR ATR in deep-learning era[J]. Remote Sensing, 2023, 15(5): 1454. doi: 10.3390/rs15051454. [16] KEYDEL E R, LEE S W, and MOORE J T. MSTAR extended operating conditions: A tutorial[C]. SPIE 2757, Algorithms for Synthetic Aperture Radar Imagery III, Orlando, USA, 1996: 228–242. doi: 10.1117/12.242059. [17] 王璇. 分辨率与SAR目标检测分类性能的关联性研究[D]. [硕士论文], 电子科技大学, 2012.WANG Xuan. Research on the correlation between resolution and SAR target detection and classification performance[D]. [Master dissertation], University of Electronic Science and Technology of China, 2012. [18] CASTEEL JR C H, GORHAM L A, MINARDI M J, et al. A challenge problem for 2D/3D imaging of targets from a volumetric data set in an urban environment[C]. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, Orlando, USA, 2007: 97–103. doi: 10.1117/12.731457. [19] ERTIN E, AUSTIN C D, SHARMA S, et al. GOTCHA experience report: Three-dimensional SAR imaging with complete circular apertures[C]. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, Orlando, USA, 2007: 9–20. doi: 10.1117/12.723245. [20] 朱岱寅, 耿哲, 俞翔, 等. 地面目标多角度SAR数据集构建与目标识别方法[J]. 南京航空航天大学学报, 2022, 54(5): 985–994. doi: 10.16356/j.1005-2615.2022.05.022.ZHU Daiyin, GENG Zhe, YU Xiang, et al. SAR database construction for ground targets at multiple angles and target recognition method[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2022, 54(5): 985–994. doi: 10.16356/j.1005-2615.2022.05.022. [21] SUN Xian, LV Yixuan, WANG Zhirui, et al. Scan: Scattering characteristics analysis network for few-shot aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226517. doi: 10.1109/TGRS.2022.3166174. [22] HUANG Lanqing, LIU Bin, LI Boying, et al. Opensarship: A dataset dedicated to sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672. [23] LI Boying, LIU Bin, HUANG Lanqing, et al. OpenSARShip 2.0: A large-volume dataset for deeper interpretation of ship targets in sentinel-1 imagery[C]. 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), Beijing, China, 2017: 1–5. doi: 10.1109/BIGSARDATA.2017.8124929. [24] HOU Xiyue, AO Wei, SONG Qian, et al. FUSAR-ship: Building a high-resolution SAR-AIS matchup dataset of gaofen-3 for ship detection and recognition[J]. Science China Information Sciences, 2020, 63(4): 140303. doi: 10.1007/s11432-019-2772-5. [25] MALMGREN-HANSEN D, KUSK A, DALL J, et al. Improving SAR automatic target recognition models with transfer learning from simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(9): 1484–1488. doi: 10.1109/LGRS.2017.2717486. [26] LEWIS B, SCARNATI T, SUDKAMP E, et al. A SAR dataset for ATR development: The synthetic and measured paired labeled experiment (SAMPLE)[C]. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, Baltimore, USA, 2019: 39–54. doi: 10.1117/12.2523460. [27] ZHANG Jinsong, XING Mengdao, and XIE Yiyuan. FEC: A feature fusion framework for SAR target recognition based on electromagnetic scattering features and deep CNN features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(3): 2174–2187. doi: 10.1109/TGRS.2020.3003264. [28] ZHANG Tianwen, ZHANG Xiaoling, KE Xiao, et al. HOG-ShipCLSNet: A novel deep learning network with HOG feature fusion for SAR ship classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210322. doi: 10.1109/TGRS.2021.3082759. [29] WAGNER S A. SAR ATR by a combination of convolutional neural network and support vector machines[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 2861–2872. doi: 10.1109/TAES.2016.160061. [30] 王汝意, 张汉卿, 韩冰, 等. 基于角度内插仿真的飞机目标多角度SAR数据集构建方法研究[J]. 雷达学报, 2022, 11(4): 637–651. doi: 10.12000/jr21193.WANG Ruyi, ZHANG Hanqing, HAN Bing, et al. Multiangle SAR dataset construction of aircraft targets based on angle interpolation simulation[J]. Journal of Radars, 2022, 11(4): 637–651. doi: 10.12000/JR21193. doi: 10.12000/jr21193. [31] LIU Lei, PAN Zongxu, QIU Xiaolan, et al. SAR target classification with CycleGAN transferred simulated samples[C]. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 4411–4414. doi: 10.1109/IGARSS.2018.8517866. [32] HE Qishan, ZHAO Lingjun, JI Kefeng, et al. SAR target recognition based on task-driven domain adaptation using simulated data[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4019205. doi: 10.1109/LGRS.2021.3116707. [33] CHEN Zhuo, ZHAO Lingjun, HE Qishan, et al. Pixel-level and feature-level domain adaptation for heterogeneous SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4515205. doi: 10.1109/LGRS.2022.3214750. [34] SHI Yu, DU Lan, GUO Yuchen, et al. Unsupervised domain adaptation based on progressive transfer for ship detection: From optical to SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5230317. doi: 10.1109/TGRS.2022.3185298. [35] 化盈盈, 张岱墀, 葛仕明. 深度学习模型可解释性的研究进展[J]. 信息安全学报, 2020, 5(3): 1–12. doi: 10.19363/J.cnki.cn10-1380/tn.2020.05.01.HUA Yingying, ZHANG Daichi, and GE Shiming. Research progress in the interpretability of deep learning models[J]. Journal of Cyber Security, 2020, 5(3): 1–12. doi: 10.19363/J.cnki.cn10-1380/tn.2020.05.01. [36] 徐丰, 金亚秋. 微波视觉与SAR图像智能解译[J]. 雷达学报, 2024, 13(2): 285–306. doi: 10.12000/JR23225.XU Feng and JIN Yaqiu. Microwave vision and intelligent perception of radar imagery[J]. Journal of Radars, 2024, 13(2): 285–306. doi: 10.12000/JR23225. [37] XU Feng and ZHANG Xu. On the concept of semantic electromagnetics[C]. 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China), Xuzhou, China, 2022: 1–3. doi: 10.1109/ACES-China56081.2022.10065038. [38] ZHANG Tianwen and ZHANG Xiaoling. Injection of traditional hand-crafted features into modern CNN-based models for SAR ship classification: What, why, where, and how[J]. Remote Sensing, 2021, 13(11): 2091. doi: 10.3390/rs13112091. [39] HUANG Zhongling, YAO Xiwen, LIU Ying, et al. Physically explainable CNN for SAR image classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 190: 25–37. doi: 10.1016/j.isprsjprs.2022.05.008. [40] POTTER L C and MOSES R L. Attributed scattering centers for SAR ATR[J]. IEEE Transactions on Image Processing, 1997, 6(1): 79–91. doi: 10.1109/83.552098. [41] DING Baiyuan, WEN Gongjian, HUANG Xiaohong, et al. Target recognition in synthetic aperture radar images via matching of attributed scattering centers[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7): 3334–3347. doi: 10.1109/JSTARS.2017.2671919. [42] DING Baiyuan, WEN Gongjian, HUANG Xiaohong, et al. Data augmentation by multilevel reconstruction using attributed scattering center for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(6): 979–983. doi: 10.1109/LGRS.2017.2692386. [43] WU Min, XING Mengdao, ZHANG Lei, et al. Super-resolution imaging algorithm based on attributed scattering center model[C]. 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi’an, China, 2014: 271–275. doi: 10.1109/ChinaSIP.2014.6889246. [44] LIU Hongwei, JIU Bo, LI Fei, et al. Attributed scattering center extraction algorithm based on sparse representation with dictionary refinement[J]. IEEE Transactions on Antennas and Propagation, 2017, 65(5): 2604–2614. doi: 10.1109/TAP.2017.2673764. [45] ZHOU Yu, LI Yi, XIE Weitong, et al. A convolutional neural network combined with attributed scattering centers for SAR ATR[J]. Remote Sensing, 2021, 13(24): 5121. doi: 10.3390/rs13245121. [46] LIU Zhunga, WANG Longfei, WEN Zaidao, et al. Multilevel scattering center and deep feature fusion learning framework for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5227914. doi: 10.1109/TGRS.2022.3174703. [47] FENG Sijia, JI Kefeng, ZHANG Linbin, et al. SAR target classification based on integration of asc parts model and deep learning algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 10213–10225. doi: 10.1109/JSTARS.2021.3116979. [48] FENG Sijia, JI Kefeng, WANG Fulai, et al. PAN: Part attention network integrating electromagnetic characteristics for interpretable SAR vehicle target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5204617. doi: 10.1109/TGRS.2023.3256399. [49] CHOI J H, LEE M J, JEONG N H, et al. Fusion of target and shadow regions for improved SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226217. doi: 10.1109/TGRS.2022.3165849. [50] LI Feng, YI Min, ZHANG Chaoqi, et al. POLSAR target recognition using a feature fusion framework based on monogenic signal and complex-valued nonlocal network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 7859–7872. doi: 10.1109/JSTARS.2022.3194551. [51] LI Feng, ZHANG Chaoqi, ZHANG Xin, et al. MF-DCMANet: A multi-feature dual-stage cross manifold attention network for PolSAR target recognition[J]. Remote Sensing, 2023, 15(9): 2292. doi: 10.3390/rs15092292. [52] LANG Haitao, WU Siwen, and XU Yongjie. Ship classification in SAR images improved by AIS knowledge transfer[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(3): 439–443. doi: 10.1109/LGRS.2018.2792683. [53] XING Xiangwei, JI Kefeng, ZOU Huanxin, et al. Ship classification in TerraSAR-X images with feature space based sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1562–1566. doi: 10.1109/LGRS.2013.2262073. [54] MARGARIT G and TABASCO A. Ship classification in single-pol SAR images based on fuzzy logic[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(8): 3129–3138. doi: 10.1109/TGRS.2011.2112371. [55] 吕艺璇, 王智睿, 王佩瑾, 等. 基于散射信息和元学习的SAR图像飞机目标识别[J]. 雷达学报, 2022, 11(4): 652–665. doi: 10.12000/JR22044.LYU Yixuan, WANG Zhirui, WANG Peijin et al. Scattering information and meta-learning based SAR images interpretation for aircraft target recognition[J]. Journal of Radars, 2022, 11(4): 652–665. doi: 10.12000/JR22044. [56] KANG Yuzhuo, WANG Zhirui, ZUO Haoyu, et al. ST-Net: Scattering topology network for aircraft classification in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5202117. doi: 10.1109/TGRS.2023.3236987. [57] KARPATNE A, ATLURI G, FAGHMOUS J H, et al. Theory-guided data science: A new paradigm for scientific discovery from data[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(10): 2318–2331. doi: 10.1109/TKDE.2017.2720168. [58] HUANG Zhongling, DATCU M, PAN Zongxu, et al. Deep SAR-Net: Learning objects from signals[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 179–193. doi: 10.1016/j.isprsjprs.2020.01.016. [59] HUANG Zhongling, DATCU M, PAN Zongxu, et al. A hybrid and explainable deep learning framework for SAR images[C]. IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, USA, 2020: 1727–1730. doi: 10.1109/IGARSS39084.2020.9323845. [60] HUANG Zhongling, DUMITRU C O, and REN Jun. Physics-aware feature learning of sar images with deep neural networks: A case study[C]. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021: 1264–1267. doi: 10.1109/IGARSS47720.2021.9554842. [61] LEE J S, GRUNES M R, AINSWORTH T L, et al. Unsupervised classification using polarimetric decomposition and the complex wishart classifier[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2249–2258. doi: 10.1109/36.789621. [62] LIU Jiaming, XING Mengdao, YU Hanwen, et al. EFTL: Complex convolutional networks with electromagnetic feature transfer learning for SAR target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5209811. doi: 10.1109/TGRS.2021.3083261. [63] FENG Sijia, JI Kefeng, MA Xiaojie, et al. Target region segmentation in SAR vehicle chip image with ACM net[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4014605. doi: 10.1109/LGRS.2021.3085188. [64] FENG Sijia, JI Kefeng, WANG Fulai, et al. Electromagnetic scattering feature (ESF) module embedded network based on ASC model for robust and interpretable SAR ATR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5235415. doi: 10.1109/TGRS.2022.3208333. [65] HUANG Zhongling, WU Chong, YAO Xiwen, et al. Physics inspired hybrid attention for SAR target recognition[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 207: 164–174. doi: 10.1016/j.isprsjprs.2023.12.004. [66] 黄钟泠, 吴冲, 姚西文, 等. 基于时频分析的SAR目标微波视觉特性智能感知方法与应用[J]. 雷达学报, 2024, 13(2): 331–344. doi: 10.12000/jr23191.HUANG Zhongling, WU Chong, YAO Xiwen et al. Physically explainable intelligent perception and application of SAR target characteristics based on time-frequency analysis[J]. Journal of Radars, 2024, 13(2): 331–344. doi: 10.12000/JR23191. doi: 10.12000/jr23191. [67] TAI T, TODA M, SENZAKI K, et al. Leveraging physics-guided features for domain adaptation in SAR target classification[C]. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, USA, 2023: 6001–6004. doi: 10.1109/IGARSS52108.2023.10283259. [68] DING Jun, CHEN Bo, LIU Hongwei, et al. Convolutional neural network with data augmentation for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 364–368. doi: 10.1109/LGRS.2015.2513754. [69] GUO Jiayi, LEI Bin, DING Chibiao, et al. Synthetic aperture radar image synthesis by using generative adversarial nets[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1111–1115. doi: 10.1109/LGRS.2017.2699196. [70] ZHANG Mingrui, CUI Zongyong, WANG Xianyuan, et al. Data augmentation method of SAR image dataset[C]. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 5292–5295. doi: 10.1109/IGARSS.2018.8518825. [71] 张明蕊. SAR图像数据分集与扩容方法研究[D]. [硕士论文], 电子科技大学, 2019.ZHANG Mingrui. Research of SAR image data diversity and data augmentation method[D]. [Master dissertation], University of Electronic Science and Technology of China, 2019. [72] DING Baiyuan and WEN Gongjian. Target recognition of SAR images based on multi-resolution representation[J]. Remote Sensing Letters, 2017, 8(11): 1006–1014. doi: 10.1080/2150704X.2017.1346397. [73] YAN Yue. Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition[J]. Journal of Electronic Imaging, 2018, 27(2): 023024. doi: 10.1117/1.JEI.27.2.023024. [74] WANG Ruonan, WANG Zhaocheng, XIA Kewen, et al. Target recognition in single-channel SAR images based on the complex-valued convolutional neural network with data augmentation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(2): 796–804. doi: 10.1109/TAES.2022.3190804. [75] DOO S H, SMITH G, and BAKER C. Target classification performance as a function of measurement uncertainty[C]. 2015 IEEE 5th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR), Singapore, 2015: 587–590. doi: 10.1109/APSAR.2015.7306277. [76] KWAK Y, SONG W J, and KIM S E. Speckle-noise-invariant convolutional neural network for SAR target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4): 549–553. doi: 10.1109/LGRS.2018.2877599. [77] YANG Minjia, BAI Xueru, WANG Li, et al. Mixed loss graph attention network for few-shot SAR target classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5216613. doi: 10.1109/TGRS.2021.3124336. [78] LI Weijie, YANG Wei, ZHANG Wenpeng, et al. Hierarchical disentanglement-alignment network for robust SAR vehicle recognition[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 9661–9679. doi: 10.1109/JSTARS.2023.3324182. [79] DING Baiyuan and WEN Gongjian. Exploiting multi-view SAR images for robust target recognition[J]. Remote Sensing, 2017, 9(11): 1150. doi: 10.3390/rs9111150. [80] LV Junya and LIU Yue. Data augmentation based on attributed scattering centers to train robust CNN for SAR ATR[J]. IEEE Access, 2019, 7: 25459–25473. doi: 10.1109/ACCESS.2019.2900522. [81] CRESWELL A, WHITE T, DUMOULIN V, et al. Generative adversarial networks: An overview[J]. IEEE Signal Processing Magazine, 2018, 35(1): 53–65. doi: 10.1109/MSP.2017.2765202. [82] AUER S, BAMLER R, and REINARTZ P. RaySAR-3D SAR simulator: Now open source[C]. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016: 6730–6733. doi: 10.1109/IGARSS.2016.7730757. [83] GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750. [84] BEN-DAVID S, BLITZER J, CRAMMER K, et al. Analysis of representations for domain adaptation[M]. SCHÖLKOPF B, PLATT J, and HOFMANN T. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference. Cambridge: The MIT Press, 2007, 19: 137–144. doi: 10.7551/mitpress/7503.003.0022. [85] MORENO-TORRES J G, RAEDER T, ALAIZ-RODRíGUEZ R, et al. A unifying view on dataset shift in classification[J]. Pattern Recognition, 2012, 45(1): 521–530. doi: 10.1016/j.patcog.2011.06.019. [86] BEN-DAVID S, BLITZER J, CRAMMER K, et al. A theory of learning from different domains[J]. Machine Learning, 2010, 79(1/2): 151–175. doi: 10.1007/s10994-009-5152-4. [87] LONG Mingsheng, CAO Yue, CAO Zhangjie, et al. Transferable representation learning with deep adaptation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(12): 3071–3085. doi: 10.1109/TPAMI.2018.2868685. [88] SUN Baochen and SAENKO K. Deep CORAL: Correlation alignment for deep domain adaptation[C]. 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 443–450. doi: 10.1007/978-3-319-49409-8_35. [89] ZELLINGER W, GRUBINGER T, LUGHOFER E, et al. Central moment discrepancy (CMD) for domain-invariant representation learning[C]. 5th International Conference on Learning Representations, Toulon, France, 2017. [90] GANIN Y and LEMPITSKY V. Unsupervised domain adaptation by backpropagation[C]. The 32nd International Conference on Machine Learning, Lile, France, 2015: 1180–1189. [91] SHEN Jian, QU Yanru, ZHANG Weinan, et al. Wasserstein distance guided representation learning for domain adaptation[C]. The 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 4058–4065. doi: 10.1609/aaai.v32i1.11784. [92] GHIFARY M, KLEIJN W B, ZHANG Mengjie, et al. Domain generalization for object recognition with multi-task autoencoders[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 2551–2559. doi: 10.1109/iccv.2015.293. [93] LI Da, ZHANG Jianshu, YANG Yongxin, et al. Episodic training for domain generalization[C]. 2019 IEEE/CVF International Conference on Computer Vision, Seoul, Korea, 2019: 1446–1455. doi: 10.1109/iccv.2019.00153. [94] ZHOU Kaiyang, YANG Yongxin, CAVALLARO A, et al. Learning generalisable omni-scale representations for person re-identification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5056–5069. doi: 10.1109/TPAMI.2021.3069237. [95] SHAO Rui, LAN Xiangyuan, LI Jiawei, et al. Multi-adversarial discriminative deep domain generalization for face presentation attack detection[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA, 2019: 10015–10023. doi: 10.1109/CVPR.2019.01026. [96] WANG Zhen, WANG Qiansheng, LV Chengguo, et al. Unseen target stance detection with adversarial domain generalization[C]. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020: 1–8. doi: 10.1109/IJCNN48605.2020.9206635. [97] 李理, 孙玉林, 曹然, 等. 基于联合分布适配的水下声源测距算法研究[J]. 电子与信息学报, 2022, 44(6): 2061–2070. doi: 10.11999/JEIT211418.LI Li, SUN Yulin, CAO Ran, et al. Research on underwater source ranging algorithm based on joint distribution adaptation[J]. Journal of Electronics & Information Technology, 2022, 44(6): 2061–2070. doi: 10.11999/JEIT211418. [98] 范苍宁, 刘鹏, 肖婷, 等. 深度域适应综述: 一般情况与复杂情况[J]. 自动化学报, 2021, 47(3): 515–548. doi: 10.16383/j.aas.c200238.FAN Cangning, LIU Peng, XIAO Ting, et al. A review of deep domain adaptation: General situation and complex situation[J]. Acta Automatica Sinica, 2021, 47(3): 515–548. doi: 10.16383/j.aas.c200238. [99] ZHANG Lei and GAO Xinbo. Transfer adaptation learning: A decade survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(1): 23–44. doi: 10.1109/TNNLS.2022.3183326. [100] ZHANG Wei, ZHU Yongfeng, and FU Qiang. Adversarial deep domain adaptation for multi-band SAR images classification[J]. IEEE Access, 2019, 7: 78571–78583. doi: 10.1109/ACCESS.2019.2922844. [101] ZHANG Yukun, GUO Xiansheng, LEUNG H, et al. Transfer learning with shared and specific structures for SAR target recognition[C]. IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 1003–1006. doi: 10.1109/IGARSS46834.2022.9883216. [102] CHEN Ting, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations[C]. The 37th International Conference on Machine Learning, 2020: 1597–1607. [103] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. Active learning SAR image classification method crossing different imaging platforms[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4514105. doi: 10.1109/LGRS.2022.3208468. [104] ZHAO Siyuan, XU Yin, LUO Ying, et al. A domain adaptation network for cross-imaging satellites sar image ship classification[C]. IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 1580–1583. doi: 10.1109/IGARSS46834.2022.9883273. [105] ZHAO Siyuan, ZHANG Zenghui, ZHANG Tao, et al. Transferable SAR image classification crossing different satellites under open set condition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4506005. doi: 10.1109/LGRS.2022.3159179. [106] GU Xiang, SUN Jian, and XU Zongben. Spherical space domain adaptation with robust pseudo-label loss[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 9098–9107. doi: 10.1109/CVPR42600.2020.00912. [107] GAO Zhiqiang, ZHANG Shufei, HUANG Kaizhu, et al. Gradient distribution alignment certificates better adversarial domain adaptation[C]. 2021 IEEE/CVF International Conference on Computer Vision, Montreal, Canada, 2021: 8917–8926. doi: 10.1109/ICCV48922.2021.00881. [108] ZOU Bin, QIN Jiang, and ZHANG Lamei. Cross-scene target detection based on feature adaptation and uncertainty-aware pseudo-label learning for high resolution SAR images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 200: 173–190. doi: 10.1016/j.isprsjprs.2023.05.009. [109] SHI Yu, DU Lan, and GUO Yuchen. Unsupervised domain adaptation for SAR target detection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 6372–6385. doi: 10.1109/JSTARS.2021.3089238. [110] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. A feature decomposition-based method for automatic ship detection crossing different satellite SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5234015. doi: 10.1109/TGRS.2022.3201628. [111] ZHAO Siyuan, ZHANG Zenghui, GUO Weiwei, et al. An automatic ship detection method adapting to different satellites SAR images with feature alignment and compensation loss[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225217. doi: 10.1109/TGRS.2022.3160727. [112] ZHAO Siyuan, LUO Ying, ZHANG Tao, et al. A domain specific knowledge extraction transformer method for multisource satellite-borne SAR images ship detection[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2023, 198: 16–29. doi: 10.1016/j.isprsjprs.2023.02.011. [113] ROSTAMI M, KOLOURI S, EATON E, et al. Deep transfer learning for few-shot SAR image classification[J]. Remote Sensing, 2019, 11(11): 1374. doi: 10.3390/rs11111374. [114] SONG Yucheng, LI Jingrun, GAO Peng, et al. Two-stage cross-modality transfer learning method for military-civilian SAR ship recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4506405. doi: 10.1109/LGRS.2022.3162707. [115] ZHAO Shuangmei and LANG Haitao. Improving deep subdomain adaptation by dual-branch network embedding attention module for SAR ship classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8038–8048. doi: 10.1109/JSTARS.2022.3206753. [116] GUO Yuchen, DU Lan, and LYU Guoxin. SAR target detection based on domain adaptive faster R-CNN with small training data size[J]. Remote Sensing, 2021, 13(21): 4202. doi: 10.3390/rs13214202. [117] ZHANG Jun, LI Simin, DONG Yongfeng, et al. Hierarchical similarity alignment for domain adaptive ship detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5240611. doi: 10.1109/TGRS.2022.3227626. [118] ZHANG Yukun, GUO Xiansheng, LI Lin, et al. Deep knowledge integration of heterogeneous features for domain adaptive SAR target recognition[J]. Pattern Recognition, 2022, 126: 108590. doi: 10.1016/j.patcog.2022.108590. [119] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031. [120] LEI Zhengxin, XU Feng, WEI Jiangtao, et al. SAR-NeRF: Neural radiance fields for synthetic aperture radar multi-view representation[EB/OL]. https://arxiv.org/abs/2307.05087, 2023. [121] FU Shilei and XU Feng. Differentiable SAR renderer and image-based target reconstruction[J]. IEEE Transactions on Image Processing, 2022, 31: 6679–6693. doi: 10.1109/TIP.2022.3215069. [122] 仇晓兰, 焦泽坤, 杨振礼, 等. 微波视觉三维SAR关键技术及实验系统初步进展[J]. 雷达学报, 2022, 11(1): 1–19. doi: 10.12000/JR22027.QIU Xiaolan, JIAO Zekun, YANG Zhenli, et al. Key technology and preliminary progress of microwave vision 3D SAR experimental system[J]. Journal of Radars, 2022, 11(1): 1–19. doi: 10.12000/JR22027. [123] CHANG Yupeng, WANG Xu, WANG Jindong, et al. A survey on evaluation of large language models[J]. ACM Transactions on Intelligent Systems and Technology, 2024, 15(3): 39. doi: 10.1145/3641289. [124] KIRILLOV A, MINTUN E, RAVI N, et al. Segment anything[C]. 2023 IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 3992–4003. doi: 10.1109/ICCV51070.2023.00371. [125] WOOLLARD M, BLACKNELL D, GRIFFITHS H, et al. SARCASTIC v2.0—high-performance SAR simulation for next-generation ATR systems[J]. Remote Sensing, 2022, 14(11): 2561. doi: 10.3390/rs14112561. [126] 董纯柱, 胡利平, 朱国庆, 等. 地面车辆目标高质量SAR图像快速仿真方法[J]. 雷达学报, 2015, 4(3): 351–360. doi: 10.12000/JR15057.DONG Chunzhu, HU Liping, ZHU Guoqing et al. Efficient simulation method for high quality SAR images of complex ground vehicles[J]. Journal of Radars, 2015, 4(3): 351–360. doi: 10.12000/JR15057. [127] NIU Shengren, QIU Xiaolan, LEI Bin, et al. A SAR target image simulation method with DNN embedded to calculate electromagnetic reflection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2593–2610. doi: 10.1109/JSTARS.2021.3056920. [128] WEI Jiangtao, LUOMEI Yixiang, ZHANG Xu, et al. Learning surface scattering parameters from SAR images using differentiable ray tracing[EB/OL]. https://arxiv.org/abs/2401.01175, 2024. [129] LV Xiaoling, QIU Xiaolan, YU Wenming, et al. Simulation-aided SAR target classification via dual-branch reconstruction and subdomain alignment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5214414. doi: 10.1109/TGRS.2023.3305094. [130] SHI Yu, DU Lan, LI Chen, et al. Unsupervised domain adaptation for SAR target classification based on domain- and class-level alignment: From simulated to real data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2024, 207: 1–13. doi: 10.1016/j.isprsjprs.2023.11.010. [131] GUO Qian, XU Huilin, and XU Feng. Causal adversarial autoencoder for disentangled SAR image representation and few-shot target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5221114. doi: 10.1109/TGRS.2023.3330478. [132] LI Weijie, YANG Wei, LIU Li, et al. Discovering and explaining the noncausality of deep learning in SAR ATR[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 4004605. doi: 10.1109/LGRS.2023.3266493. [133] LIU Jiaxiang, LIU Zhunga, ZHANG Zuowei, et al. A new causal inference framework for SAR target recognition[J]. IEEE Transactions on Artificial Intelligence, 2024: 1–15. doi: 10.1109/TAI.2024.3357664. -
图(9) / 表(8)
计量
- 文章访问数: 856
- HTML全文浏览量: 290
- PDF下载量: 158
- 被引次数: 0
下载:
