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 引用本文: 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 Citation: 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 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 引用本文: 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 Citation: 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 作者简介:

杜　兰(1980–)，女，河北深泽人，博士，教授。2007年在西安电子科技大学电子工程学院获得博士学位，2007年8月至2009年9月在美国杜克大学电子与计算机工程系做博士后访问研究，现担任西安电子科技大学电子工程学院教授。主要研究方向为雷达目标识别、雷达信号处理、机器学习，在IEEE Trans. SP, JMLR, ESWA, IS, PR, IEEE Trans. GRS, IEEE Trans. AES, IEEE J-STARS和NIPS等国内外期刊、知名国际会议以第一作者或通信作者发表论文80余篇，授权国家/国防专利20余项，多次在国际、国内会议做特邀报告，并多次获优秀会议论文奖。E-mail: [email protected]

王兆成(1990–)，男，山东威海人，博士，讲师。2018年在西安电子科技大学雷达信号处理国家重点实验室获得博士学位，现担任河北工业大学电子信息工程学院讲师。主要研究方向为SAR图像处理、SAR目标检测与识别、机器学习。E-mail: [email protected]

王　燕(1990–)，女，山西原平人，博士。2019年在西安电子科技大学电子信息工程学院获得博士学位，现工作于中国航空工业集团公司雷华电子技术研究所。主要研究方向为SAR图像处理、目标识别、机器学习等。E-mail: [email protected]

魏　迪(1995–)，男，陕西汉中人。西安电子科技大学雷达信号处理国家重点实验室硕士研究生，主要研究方向为SAR图像目标检测，机器学习等。E-mail: [email protected]

李　璐(1992–)，女，山东烟台人，西安电子科技大学雷达信号处理国家重点实验室博士研究生，研究方向为SAR图像解译、机器学习与人工智能、智能图像处理等。E-mail: [email protected]

通讯作者: 杜兰 [email protected]

中图分类号: TN957.51

SAR作为一种主动式微波成像传感器，以其全天时、全天候、作用距离远等独特的技术优势，成为当前对地观测的主要手段之一，在军事和民用领域发挥着十分重要的作用。随着SAR遥感技术的发展，高分辨率、高质量的SAR图像不断产生，仅依靠人工手段对感兴趣的目标进行检测、识别费时费力，因此亟需发展SAR自动目标识别(ATR)技术。典型的SAR ATR系统主要包括检测、鉴别、分类/识别3个阶段，其中，检测和鉴别阶段是整个SAR ATR系统的基础，是国内外雷达界一直开展的SAR应用基础研究之一。针对单通道SAR图像，简单场景下目标检测与鉴别已经取得了不错的结果；而在复杂场景下，杂波散射强度相对高、杂波背景非均匀和目标散射强度相对弱、分布密集等情况，使得SAR目标检测和鉴别依然是一个难点。该文对近十年左右复杂场景下单通道SAR目标检测及鉴别方法的研究进展进行了归纳总结，并分析了各类方法的特点及存在的问题，展望了未来复杂场景下单通道SAR目标检测与鉴别方法的发展趋势。

合成孔径雷达 /  复杂场景 /  目标检测 / Abstract: As an active microwave imaging sensor, Synthetic Aperture Radar (SAR) has become one of the main means of Earth observation owing to its unique technical advantages of all-day, all-weather operation and long working distance. As such, it plays a very important role in military and civilian fields. With the development of SAR remote-sensing technology, high-resolution, high-quality SAR images are produced continuously. However, manual detection and recognition of targets of interest is time-consuming and laborious, so the development of Automatic Target Recognition (ATR) technology is a matter of urgency. The typical SAR ATR system primarily comprises three stages: detection, discrimination, and classification/recognition. The detection and discrimination stages are the basis of the SAR ATR system, and research on SAR applications in the radar field has been conducted by researchers around the world. For single-channel SAR images, target detection and discrimination from simple scenes yield good results. However, in complex scenes, the clutter scattering intensity is relatively high, the clutter background is heterogenous, the target scattering intensity is relatively weak, and the target distribution is dense. These factors continue to make accurate SAR target detection and discrimination difficult. In this paper, we summarize the recent research progress on single-channel SAR target detection and discrimination methods for complex scenes, analyze the characteristics and problems associated with various methods, and consider the future development trend of single-channel SAR target detection and discrimination methods for complex scenes.

Key words: Synthetic Aperture Radar (SAR) /  Complex scenes /  Target detection /  Target discrimination  王兆成, 李璐, 杜兰, 等. 基于单极化SAR图像的舰船目标检测与分类方法[J]. 科技导报, 2017, 35(20): 86–93. doi: 10.3981/j.issn.1000-7857.2017.20.009

WANG Zhaocheng, LI Lu, DU Lan, et al . Ship detection and classification baser on single-polarization SAR images[J]. Science & Technology Review , 2017, 35(20): 86–93. doi: 10.3981/j.issn.1000-7857.2017.20.009 聂春霞. 基于复杂场景SAR图像的多目标智能检测算法研究[D]. [硕士论文], 南京航空航天大学, 2017.

NIE Chunxia. Research on multi-target intelligent detection algorithm based on SAR image of complex scene[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2017. 余文毅. 复杂场景下的SAR目标检测[D]. [硕士论文], 西安电子科技大学, 2015.

YU Wenyi. SAR target detection in the complex scene[D]. [Master dissertation], Xidian University, 2015. WARD K D. Compound representation of high resolution sea clutter[J]. Electronics Letters , 1981, 17(16): 561–563. doi: 10.1049/el:19810394 OLIVER C and QUEGAN S. Understanding Synthetic Aperture Radar Images[M]. Boston, London: Artech House, 1998. FRERY A C, MULLER H J, YANASSE C C F, et al . A model for extremely heterogeneous clutter[J]. IEEE Transactions on Geoscience and Remote Sensing , 1997, 35(3): 648–659. doi: 10.1109/36.581981 ANASTASSOPOULOS V, LAMPROPOULOS G A, DROSOPOULOS A, et al . High resolution radar clutter statistics[J]. IEEE Transactions on Aerospace and Electronic Systems , 1999, 35(1): 43–59. doi: 10.1109/7.745679 TISON C, NICOLAS J M, TUPIN F, et al . A new statistical model for markovian classification of urban areas in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 2004, 42(10): 2046–2057. doi: 10.1109/TGRS.2004.834630 代梦. 背景干扰情况下高分SAR图像车辆目标检测方法研究[D]. [硕士论文], 上海交通大学, 2017.

DAI Meng. Research on vehicle target detection method for high resolution SAR images with backgrund disturbances[D]. [Master dissertation], Shanghai Jiao Tong University, 2017. WEISS M. Analysis of some modified cell-averaging CFAR processors in multiple-target situations[J]. IEEE Transactions on Aerospace and Electronic Systems , 1982, AES-18(1): 102–114. doi: 10.1109/TAES.1982.309210 ROHLING H. Radar CFAR thresholding in clutter and multiple target situations[J]. IEEE Transactions on Aerospace and Electronic Systems , 1983, AES-19(4): 608–621. doi: 10.1109/TAES.1983.309350 何友, 关键, 孟祥伟, 等. 雷达目标检测与恒虚警处理[M]. 2版. 北京: 清华大学出版社, 2011.

HE You, GUAN Jian, MENG Xiangwei, et al. Radar Target Detection and CFAR Processing[M]. 2nd ed. Beijing: Tsinghua University Press, 2011. MEZIANI H A and SOLTANI F. Performance analysis of some CFAR detectors in homogeneous and non-homogeneous pearson-distributed clutter[J]. Signal Processing , 2006, 86(8): 2115–2122. doi: 10.1016/j.sigpro.2006.02.036 RITCEY J A and DU H. Order statistic CFAR detectors for speckled area targets in SAR[C]. Proceedings of Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers, Pacific Grove, USA, 1991: 1082–1086. SMITH M E and VARSHNEY P K. VI-CFAR: A novel CFAR algorithm based on data variability[C]. 1997 IEEE National Radar Conference, Syracuse, USA, 1997: 263–268. SMITH M E and VARSHNEY P K. Intelligent CFAR processor based on data variability[J]. IEEE Transactions on Aerospace and Electronic Systems , 2000, 36(3): 837–847. doi: 10.1109/7.869503 LI Jian and ZELNIO E G. Target detection with synthetic aperture radar[J]. IEEE Transactions on Aerospace and Electronic Systems , 1996, 32(2): 613–627. doi: 10.1109/7.489506 LOMBARDO P, SCIOTTI M, and KAPLAN L M. SAR prescreening using both target and shadow information[C]. 2001 IEEE Radar Conference, Atlanta, USA, 2001: 147–152. LEUNG H, DUBASH N, and XIE N. Detection of small objects in clutter using a GA-RBF neural network[J]. IEEE Transactions on Aerospace and Electronic Systems , 2002, 38(1): 98–118. doi: 10.1109/7.993232 LAMPROPOULOS G A and LEUNG H. CFAR detection of small manmade targets using chaotic and statistical CFAR detectors[C]. SPIE 3809, Signal and Data Processing of Small Targets, Denver, USA, 1999: 292–296. NOVAK L M, OWIRKA G J, BROWER W S, et al . The automatic target-recognition system in SAIP[J]. The Lincoln Laboratory Journal , 1997, 10(2): 187–202. GREIG D W and DENNY M. Knowledge-based methods for small object detection in SAR images[C]. The SPIE 4883, SAR Image Analysis, Modeling, and Techniques V, Crete, Greece, 2003: 121–130. HALVERSEN S D, OWIRKA G J, and NOVAK L M. New approaches for detecting groups of targets[C]. 1994 28th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 1994: 137–140. OWIRKA G J, HALVERSEN S D, HIETT M, et al. An algorithm for detecting groups of targets[C]. The International Radar Conference, Alexandria, USA, 1995: 641–643. GAO Gui, KUANG Gangyao, ZHANG Qi, et al . Fast detecting and locating groups of targets in high-resolution SAR images[J]. Pattern Recognition , 2007, 40(4): 1378–1384. doi: 10.1016/j.patcog.2006.01.019 CUI Yi, ZHOU Guangyi, YANG Jian, et al . On the iterative censoring for target detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2011, 8(4): 641–655. doi: 10.1109/LGRS.2010.2098434 陈祥, 孙俊, 尹奎英, 等. 基于CFAR级联的SAR图像舰船目标检测算法[J]. 现代雷达, 2012, 34(9): 50–54, 58. doi: 10.3969/j.issn.1004-7859.2012.09.011

CHEN Xiang, SUN Jun, YIN Kuiying, et al . An algorithm of ship target detection in SAR images based on cascaded CFAR[J]. Modern Radar , 2012, 34(9): 50–54, 58. doi: 10.3969/j.issn.1004-7859.2012.09.011 宋文青, 王英华, 刘宏伟. 高分辨SAR图像自动区域筛选目标检测算法[J]. 电子与信息学报, 2016, 38(5): 1017–1025. doi: 10.11999/JEIT150808

SONG Wenqing, WANG Yinghua, and LIU Hongwei. An automatic block-to-block censoring target detector for high resolution SAR image[J]. Journal of Electronics & Information Technology , 2016, 38(5): 1017–1025. doi: 10.11999/JEIT150808 DING Tao, ANFINSEN S N, and BREKKE C. Robust CFAR detector based on truncated statistics in multiple-target situations[J]. IEEE Transactions on Geoscience and Remote Sensing , 2016, 54(1): 117–134. doi: 10.1109/TGRS.2015.2451311 YU Wenyi, WANG Yinghua, LIU Hongwei, et al . Superpixel-based CFAR target detection for high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2016, 13(5): 730–734. doi: 10.1109/LGRS.2016.2540809 AO Wei, XU Feng, LI Yongchen, et al . Detection and discrimination of ship targets in complex background from spaceborne ALOS-2 SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(2): 536–550. doi: 10.1109/JSTARS.2017.2787573 LENG Xiangguang, JI Kefeng, XIANG Xiangwei, et al . Area ratio invariant feature group for ship detection in SAR imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(7): 2376–2388. doi: 10.1109/JSTARS.2018.2820078 GAO Gui, LIU Li, ZHAO Lingjun, et al . An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 2009, 47(6): 1685–1697. doi: 10.1109/TGRS.2008.2006504 胡睿, 孙进平, 王文光. 基于 α 稳定分布的SAR图像目标检测算法[J]. 中国图象图形学报, 2009, 14(1): 25–29. doi: 10.11834/jig.20090105

HU Rui, SUN Jinping, and WANG Wenguang. Target detection of SAR images using α stable distribution[J]. Journal of image and Graphics , 2009, 14(1): 25–29. doi: 10.11834/jig.20090105 QIN Xianxiang, ZHOU Shilin, ZOU Huanxin, et al . A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2013, 10(4): 806–810. doi: 10.1109/LGRS.2012.2224317 GAO Gui. A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2011, 8(3): 557–561. doi: 10.1109/LGRS.2010.2090492 张颢, 孟祥伟, 刘磊, 等. 改进的基于Parzen窗算法的SAR图像目标检测[J]. 计算机科学, 2015, 42(11A): 151–154.

ZHANG Hao, MENG Xiangwei, LIU Lei, et al . Improved parzen window based ship detection algorithm in SAR images[J]. Computer Science , 2015, 42(11A): 151–154. LENG Xiangguang, JI Kefeng, YANG Kai, et al . A bilateral CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2015, 12(7): 1536–1540. doi: 10.1109/LGRS.2015.2412174 HUANG Yong and LIU Fang. Detecting cars in VHR SAR images via semantic CFAR algorithm[J]. IEEE Geoscience and Remote Sensing Letters , 2016, 13(6): 801–805. doi: 10.1109/LGRS.2016.2546309 曾丽娜, 周德云, 李枭扬, 等. 基于无训练单样本有效特征的SAR目标检测[J]. 雷达学报, 2017, 6(2): 177–185. doi: 10.12000/JR16114

ZENG Li’na, ZHOU Deyun, LI Xiaoyang, et al . Novel SAR target detection algorithm using free training[J]. Journal of Radars , 2017, 6(2): 177–185. doi: 10.12000/JR16114 KANG Miao, LENG Xiangguang, LIN Zhao, et al. A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]. 2017 International Workshop on Remote Sensing with Intelligent Processing, Shanghai, China, 2017: 1–4. WALTHER D. Interactions of visual attention and object recognition: Computational modeling, algorithms, and psychophysics[D]. [Ph.D. dissertation], California Institute of Technology, 2006. ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 1998, 20(11): 1254–1259. doi: 10.1109/34.730558 BORJI A and ITTI L. State-of-the-Art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2013, 35(1): 185–207. doi: 10.1109/TPAMI.2012.89 KOCH K, MCLEAN J, SEGEV R, et al . How much the eye tells the Brain[J]. Current Biology , 2006, 16(14): 1428–1434. doi: 10.1016/j.cub.2006.05.056 BRUCE N D B and TSOTSOS J K. Saliency based on information maximization[C]. The 18th International Conference on Neural Information Processing Systems, Montreal, Canada, 2005: 155–162. ITTI L and BALDI P. Bayesian surprise attracts human attention[J]. Vision Research , 2009, 49(10): 1295–1306. doi: 10.1016/j.visres.2008.09.007 HOU Xiaodi and ZHANG Liqing. Saliency detection: A spectral residual approach[C]. 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, USA, 2007: 1–8. YU Ying, WANG Bin, and ZHANG Liming. Pulse discrete cosine transform for saliency-based visual attention[C]. The IEEE 8th International Conference on Development and Learning, Shanghai, China, 2009: 1–6. CHENG Mingming, MITRA N J, HUANG Xiaolei, et al . Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2015, 37(3): 569–582. doi: 10.1109/TPAMI.2014.2345401 WANG Zhaocheng, DU Lan, and SU Hongtao. Target detection via bayesian-morphological saliency in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 2017, 55(10): 5455–5466. doi: 10.1109/TGRS.2017.2707672 王兆成. 复杂场景下SAR图像目标检测及鉴别方法研究[D]. [博士论文], 西安电子科技大学, 2018.

WANG Zhaocheng. Study on target detection and discrimination for SAR images in complex scenes[D]. [Ph.D. dissertation], Xidian University, 2018. YU Ying, WANG Bin, and ZHANG Liming. Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images[J]. Neurocomputing , 2011, 74(11): 2008–2017. doi: 10.1016/j.neucom.2010.06.026 LIU Shuo, CAO Zongjie, and LI Jin. A SVD-based Visual Attention Detection Algorithm of SAR Image[M]. ZHANG Baoju, MU Jiasong, WAMG Wei, et al. The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Cham: Springer, 2014: 479–486. ZHAO Juanping, ZHANG Zenghui, YU Wenxian, et al . A cascade coupled convolutional neural network guided visual attention method for ship detection from SAR images[J]. IEEE Access , 2018, 6: 50693–50708. doi: 10.1109/ACCESS.2018.2869289 WANG Haipeng, XU Feng, and CHEN Shanshan. Saliency detector for SAR images based on pattern recurrence[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 9(7): 2891–2900. doi: 10.1109/JSTARS.2016.2521709 NI Weiping, MA Long, YAN Weidong, et al . Background context-aware-based SAR image saliency detection[J]. IEEE Geoscience and Remote Sensing Letters , 2018, 15(9): 1392–1396. doi: 10.1109/LGRS.2018.2838151 HOU Biao, YANG Wei, WANG Shuang, et al. SAR image ship detection based on visual attention model[C]. 2013 IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, 2013: 2003–2006. WANG Zhaocheng, DU Lan, ZHANG Peng, et al . Visual attention-based target detection and discrimination for high-resolution SAR images in complex scenes[J]. IEEE Transactions on Geoscience and Remote Sensing , 2018, 56(4): 1855–1872. doi: 10.1109/TGRS.2017.2769045 ZHAI Liang, LI Yu, and SU Yi. Inshore ship detection via saliency and context information in high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters , 2016, 13(12): 1870–1874. doi: 10.1109/LGRS.2016.2616187 LI Lu, DU Lan, and WANG Zhaocheng. Target detection based on dual-domain sparse reconstruction saliency in SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(11): 4230–4243. doi: 10.1109/JSTARS.2018.2874128 TU Song and SU Yi. Fast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 2016, 54(10): 5729–5744. doi: 10.1109/TGRS.2016.2571309 MARINO A, SANJUAN-FERRER M J, HAJNSEK I, et al . Ship detection with spectral analysis of synthetic aperture radar: A comparison of new and well-known algorithms[J]. Remote Sensing , 2015, 7(5): 5416–5439. doi: 10.3390/rs70505416 ARNAUD A. Ship detection by SAR interferometry[C]. IEEE 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 1999: 2616–2618. LENG Xiangguang, JI Kefeng, ZHOU Shilin, et al . Ship detection based on complex signal kurtosis in single-channel SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing , 2019, 57(9): 6447–6461. doi: 10.1109/TGRS.2019.2906054 EL-DARYMLI K, MOLONEY C, GILL E W, et al. Nonlinearity and the effect of detection on single-channel synthetic aperture radar imagery[C]. Proceedings of Oceans 14, Taipei, China, 2014: 1–7. EL-DARYMLI K, MCGUIRE P, GILL E W, et al . Characterization and statistical modeling of phase in single-channel synthetic aperture radar imagery[J]. IEEE Transactions on Aerospace and Electronic Systems , 2015, 51(3): 2071–2092. doi: 10.1109/TAES.2015.140711 LENG Xiangguang, JI Kefeng, ZHOU Shilin, et al . Fast shape parameter estimation of the complex generalized gaussian distribution in SAR images[J]. IEEE Geoscience and Remote Sensing Letters , (in press). doi: 10.1109/LGRS.2019.2960095 LENG Xiangguang, JI Kefeng, ZHOU Shilin, et al . Discriminating ship from radio frequency interference based on noncircularity and non-gaussianity in Sentinel-1 SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing , 2019, 57(1): 352–363. doi: 10.1109/TGRS.2018.2854661 ZHANG Zhimian, WANG Haipeng, XU Feng, et al . Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing , 2017, 55(12): 7177–7188. doi: 10.1109/TGRS.2017.2743222 BURL M C, OWIRKA G J, and NOVAK L M. Texture discrimination in synthetic aperture radar imagery[C]. The Twenty-Third Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 1989: 399–404. GAO Gui. An improved scheme for target discrimination in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , 2011, 49(1): 277–294. doi: 10.1109/TGRS.2010.2052623 刘轩, 王卫红, 唐晓斌, 等. 遗传算法在SAR图像目标鉴别特征选择上的应用[J]. 电子科技, 2014, 27(5): 140–144. doi: 10.3969/j.issn.1007-7820.2014.05.041

LIU Xuan, WANG Weihong, TANG Xiaobin, et al . Feature selection for target discrimination in SAR images based on genetic algorithm[J]. Electronic Science and Technology , 2014, 27(5): 140–144. doi: 10.3969/j.issn.1007-7820.2014.05.041 李礼. SAR目标检测与鉴别算法研究及软件设计[D]. [硕士论文], 西安电子科技大学, 2013.

LI Li. Research on SAR target detection and discrimination algorithms and software design[D]. [Master dissertation], Xidian University, 2013. AMOON M, REZAI-RAD G A, and DALIRI M R. PSO-based optimal selection of zernike moments for target discrimination in high-resolution SAR imagery[J]. Journal of the Indian Society of Remote Sensing , 2014, 42(3): 483–493. doi: 10.1007/s12524-013-0344-6 陈琪, 陆军, 王娜, 等. 一种基于SAR图像鉴别的港口区域舰船目标新方法[J]. 宇航学报, 2011, 32(12): 2582–2588. doi: 10.3873/j.issn.1000-1328.2011.12.017

CHEN Qi, LU Jun, WANG Na, et al . An SAR images-based new method for ship discrimination in harbor region[J]. Journal of Astronautics , 2011, 32(12): 2582–2588. doi: 10.3873/j.issn.1000-1328.2011.12.017 王斐. 特征变换方法及其在SAR目标鉴别上的应用[D]. [硕士论文], 西安电子科技大学, 2014.

WANG Fei. Feature transformation with applications to SAR target discrimination[D]. [Master dissertation], Xidian University, 2014. DU Lan, DAI Hui, WANG Yan, et al . Target discrimination based on weakly supervised learning for high-resolution sar images in complex scenes[J]. IEEE Transactions on Geoscience and Remote Sensing , 2020, 58(1): 461–472. doi: 10.1109/TGRS.2019.2937175 WANG Zhaocheng, DU Lan, and SU Hongtao. Superpixel-level target discrimination for high-resolution SAR images in complex scenes[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(9): 3127–3143. doi: 10.1109/JSTARS.2018.2850043 WANG Ning, WANG Yinghua, LIU Hongwei, et al . Feature-fused SAR target discrimination using multiple convolutional neural networks[J]. IEEE Geoscience and Remote Sensing Letters , 2017, 14(10): 1695–1699. doi: 10.1109/LGRS.2017.2729159 WANG Yan, DU Lan, and DAI Hui. Target discrimination method for SAR images based on semisupervised co-training[J]. Journal of Applied Remote Sensing , 2018, 12(1): 015004. DU Lan, WANG Yan, XIE Weitong, et al . A semisupervised infinite latent Dirichlet allocation model for target discrimination in SAR images with complex scenes[J]. IEEE Transactions on Geoscience and Remote Sensing , 2020, 58(1): 666–679. doi: 10.1109/TGRS.2019.2939001 [100] DENG Li. The MNIST database of handwritten digit images for machine learning research[Best of the Web][J]. IEEE Signal Processing Magazine , 2012, 29(6): 141–142. doi: 10.1109/MSP.2012.2211477 [101] CARVALHO E F and ENGEL P M. Convolutional sparse feature descriptor for object recognition in CIFAR-10[C]. 2013 Brazilian Conference on Intelligent Systems, Fortaleza, Brazil, 2013: 131–135. [102] DENG Jia, DONG Wei, SOCHER R, et al. ImageNet: A Large-Scale Hierarchical Image Database[C]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 248–255. [103] 杜兰, 刘彬, 王燕, 等. 基于卷积神经网络的SAR图像目标检测算法[J]. 电子与信息学报, 2016, 38(12): 3018–3025. doi: 10.11999/JEIT161032

DU Lan, LIU Bin, WANG Yan, et al . Target detection method based on convolutional neural network for SAR image[J]. Journal of Electronics & Information Technology , 2016, 38(12): 3018–3025. doi: 10.11999/JEIT161032 [104] WANG Zhaocheng, DU Lan, MAO Jiashun, et al . SAR target detection based on SSD with data augmentation and transfer learning[J]. IEEE Geoscience and Remote Sensing Letters , 2019, 16(1): 150–154. doi: 10.1109/LGRS.2018.2867242 [105] 王思雨, 高鑫, 孙皓, 等. 基于卷积神经网络的高分辨率SAR图像飞机目标检测方法[J]. 雷达学报, 2017, 6(2): 195–203. doi: 10.12000/JR17009

WANG Siyu, GAO Xin, SUN Hao, et al . An aircraft detection method based on convolutional neural networks in high-resolution SAR images[J]. Journal of Radars , 2017, 6(2): 195–203. doi: 10.12000/JR17009 [106] COZZOLINO D, DI MARTINO G, POGGI G, et al. A fully convolutional neural network for low-complexity single-stage ship detection in Sentinel-1 SAR images[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, USA, 2017: 886–889. [107] 李健伟, 曲长文, 彭书娟, 等. 基于卷积神经网络的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2018, 40(9): 1953–1959. doi: 10.3969/j.issn.1001-506X.2018.09.09

LI Jianwei, QU Changwen, PENG Shujuan, et al . Ship detection in SAR images based on convolutional neural network[J]. Systems Engineering and Electronics , 2018, 40(9): 1953–1959. doi: 10.3969/j.issn.1001-506X.2018.09.09 [108] JIAO Jiao, ZHANG Yue, SUN Hao, et al . A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection[J]. IEEE Access , 2018, 6: 20881–20892. doi: 10.1109/ACCESS.2018.2825376 [109] LIU Lei, CHEN Guowei, PAN Zongxu, et al. Inshore ship detection in SAR images based on deep neural networks[C]. 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018: 25–28. [110] 李健伟, 曲长文, 彭书娟, 等. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测[J]. 电子与信息学报, 2019, 41(1): 143–149. doi: 10.11999/JEIT180050

LI Jianwei, QU Changwen, PENG Shujuan, et al . Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics & Information Technology , 2019, 41(1): 143–149. doi: 10.11999/JEIT180050 [111] 孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0: 高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852–862. doi: 10.12000/JR19097

SUN Xian, WANG Zhirui, SUN Yuanrui, et al . AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars , 2019, 8(6): 852–862. doi: 10.12000/JR19097 [112] ZHAO Juanping, GUO Weiwei, ZHANG Zenghui, et al . A coupled convolutional neural network for small and densely clustered ship detection in SAR images[J]. Science China Information Sciences , 2019, 62(4): 42301. doi: 10.1007/s11432-017-9405-6 [113] 陈慧元, 刘泽宇, 郭炜炜, 等. 基于级联卷积神经网络的大场景遥感图像舰船目标快速检测方法[J]. 雷达学报, 2019, 8(3): 413–424. doi: 10.12000/JR19041

CHEN Huiyuan, LIU Zeyu, GUO Weiwei, et al . Fast detection of ship targets for large-scale remote sensing image based on a cascade convolutional neural network[J]. Journal of Radars , 2019, 8(3): 413–424. doi: 10.12000/JR19041 [114] DU Lan, LI Lu, WEI Di, et al . Saliency-guided single shot multibox detector for target detection in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing , (in press). doi: 10.1109/TGRS.2019.2953936 [115] 杜兰, 魏迪, 李璐, 等. 基于半监督学习的SAR目标检测网络[J]. 电子与信息学报, 2020, 42(1): 154–163. doi: 10.11999/JEIT190783

DU Lan, WEI Di, LI Lu, et al . SAR target detection network via semi-supervised learning[J]. Journal of Electronics & Information Technology , 2020, 42(1): 154–163. doi: 10.11999/JEIT190783