Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0400005(2021)

Survey of Ship Detection in SAR Images Based on Deep Learning

Xiaohan Hou*, Guodong Jin*, and Lining Tan
Author Affiliations
  • College of Nuclear Engineering, Rocket Army Engineering University, Xi’an, Shaanxi 710025, China
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    References(54)

    [2] 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 (IGARSS), July 23-28, 2017, Fort Worth, TX, USA. New York: IEEE, 886-889(2017).

    [3] Kang M, Leng X, Lin Z. 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, May 18-21, 2017, Shanghai, China. New York: IEEE, 16981074(2017).

    [4] Wang Y Y, Wang C, Zhang H. Combining single shot multibox detector with transfer learning for ship detection using Sentinel-1 images. [C]∥2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), November 13-14, 2017, Beijing, China. New York: IEEE, 17413066(2017).

    [5] Tings B, Bentes C, Velotto D et al. Modelling ship detectability depending on TerraSAR-X-derived Metocean parameters[J]. CEAS Space Journal, 11, 81-94(2019).

    [8] Gao G, Gao S, He J et al. Ship detection using compact polarimetric SAR based on the notch filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 5380-5393(2018).

    [10] Smith M E, Varshney P K. Vi-CFAR: a novel CFAR algorithm based on data variability. [C]∥ Proceedings of the 1997 IEEE National Radar Conference, May 13-15, 2017, Syracuse, NY, USA. New York: IEEE, 5716383(2017).

    [11] Gao G, Liu L, Zhao L 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, 47, 1685-1697(2009).

    [14] Huang X, Yang W, Zhang H et al. Automatic ship detection in sar images using multi-scale heterogeneities and an a contrario decision[J]. Remote Sensing, 7, 7695-7711(2015).

    [15] Souyris J C, Henry C, Adragna F. On the use of complex SAR image spectral analysis for target detection: assessment of polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 41, 2725-2734(2003).

    [18] Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. [C]∥Proceedings of the 25th International Conference on Neural Information Processing Systems, December 3-8, 2012, Lake Tahoe, NV, USA. New York: Curran Associates Inc, 1097-1105(2012).

    [19] Goodfellow I, Bengio Y, Courville A[M]. Deep learning, 16(2016).

    [20] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).

    [21] He J L, Wang Y H, Liu H W et al. A novel automatic PolSAR ship detection method based on superpixel-level local information measurement[J]. IEEE Geoscience and Remote Sensing Letters, 15, 384-388(2018).

    [23] Ren S Q, He K M, 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, 39, 1137-1149(2017).

    [24] Liu W, Anguelov D, Erhan D et al. -12-29)[2020-07-07]. https:∥arxiv., org/abs/1512, 02325(2016).

    [25] Redmon J. -04-08)[2020-07-07]. https:∥arxiv., org/abs/1804, 02767(2018).

    [26] Sun C, Shrivastava A, Singh S et al. Revisiting unreasonable effectiveness of data in deep learning era. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 843-852(2017).

    [27] Huang L Q, Liu B, Li B Y et al. Open SARShip: a dataset dedicated to sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 195-208(2018).

    [28] Li J W, Qu C W, Shao J Q. Ship detection in SAR images based on an improved Faster R-CNN. [C]∥2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA), November 13-14, 2017, Beijing, China. New York: IEEE, 17413068(2017).

    [32] Hua Q L, Huang B, Chen X F et al. Ship target recognition algorithms based on complex domain CNN[J]. Command Information System and Technology, 10, 71-75(2019).

    [33] Goodfellow I. Pouget-abadie J, Mirza M, et al. Generative adversarial nets. [C]∥ Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec, Canada. New York: Curran Associates Inc, 2672-2680(2014).

    [35] Isola P, Zhu J Y, Zhou T H et al. -11-26)[2020-07-07]. https: ∥arxiv., org/abs/1611, 07004(2018).

    [37] Wang X L, Shrivastava A, Gupta A. A-Fast-RCNN: hard positive generation via adversary for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 3039-3048(2017).

    [39] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 936-944(2017).

    [44] He P, Huang W, He T et al. Single shot text detector with regional attention. [C]∥Single shot text detector with regional attention, October 22-29, 2017, Venice, Italy. New York: IEEE, 17453216(2017).

    [46] Bell S, Zitnick C L, Bala K et al. Inside-outside net: detecting objects in context with skip pooling and recurrent neural networks. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2874-2883(2016).

    [47] Ding J, Xue N, Long Y et al. -12-01)[2020-07-07]. https:∥arxiv.org/abs/1812.00155v1.(2018).

    [49] Cai Z, Vasconcelos N. Cascade R-CNN: delving into high quality object detection. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, UT, USA. New York: IEEE, 6154-6162(2018).

    [51] Zhou X Y, Wang D Q, Krähenbühl P[2020-07-07]. Objects as points [2020-07-07].https:∥www.researchgate.net/publication/332463177_Objects_as_Points..

    [54] He K M, Girshick R B, Dollár P. Rethinking ImageNet pre-training. [C]∥ 2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea. New York: IEEE, 4917-4926(2018).

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    Xiaohan Hou, Guodong Jin, Lining Tan. Survey of Ship Detection in SAR Images Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0400005

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    Paper Information

    Category: Reviews

    Received: Jul. 8, 2020

    Accepted: Aug. 13, 2020

    Published Online: Feb. 24, 2021

    The Author Email: Hou Xiaohan (jinguodong_army@163.com), Jin Guodong (jinguodong_army@163.com)

    DOI:10.3788/LOP202158.0400005

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