• Laser & Optoelectronics Progress
  • Vol. 58, Issue 4, 0400005 (2021)
Xiaohan Hou*, Guodong Jin*, and Lining Tan
Author Affiliations
  • College of Nuclear Engineering, Rocket Army Engineering University, Xi’an, Shaanxi 710025, China
  • show less
    DOI: 10.3788/LOP202158.0400005 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    References

    [1] Tang X, Shen J, Lu X Y. ACSI-SAR algorithm for clutter mitigation based on median canceller[J]. Command Information System and Technology, 4, 60-64, 79(2013).

    [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).

    [6] Mazzarella F, Vespe M, Santamaria C. SAR ship detection and self-reporting data fusion based on traffic knowledge[J]. IEEE Geoscience and Remote Sensing Letters, 12, 1685-1689(2015). http://ieeexplore.ieee.org/document/7093130

    [7] Lang H T, Wu S W, Xu Y J. Ship classification in SAR images improved by AIS knowledge transfer[J]. IEEE Geoscience and Remote Sensing Letters, 25, 439-443(2018). http://www.researchgate.net/publication/322617915_Ship_Classification_in_SAR_Images_Improved_by_AIS_Knowledge_Transfer/download

    [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).

    [9] Huo W B, Huang Y L, Pei J F et al. Ship detection from ocean SAR image based on local contrast variance weighted information entropy[J]. Sensors, 18, 1196(2018). http://www.ncbi.nlm.nih.gov/pubmed/29652863

    [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).

    [12] Farrouki A, Barkat M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments[J]. IEE Proceedings- Radar, Sonar and Navigation, 152, 43-51(2005). http://ieeexplore.ieee.org/document/1393512

    [13] El-Darymli K, Gill E W. McGuire P, et al. Automatic target recognition in synthetic aperture radar imagery: a state-of-the-art review[J]. IEEE Access, 4, 6014-6058(2016). http://www.ingentaconnect.com/content/iee/21693536/2016/00000004/00000001/art00200

    [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).

    [16] Ouchi K, Tamaki S, Yaguchi H et al. Ship detection based on coherence images derived from cross correlation of multilook SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 1, 184-187(2004). http://ieeexplore.ieee.org/document/1315628/citations?tabFilter=papers

    [17] Kaplan L M. Improved SAR target detection via extended fractal features[J]. IEEE Transactions on Aerospace and Electronic Systems, 37, 436-451(2001). http://ieeexplore.ieee.org/document/937460/references

    [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).

    [22] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1798-1828(2013).

    [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).

    [29] Wang Y Y, Wang C, Zhang H et al. A SAR dataset of ship detection for deep learning under complex backgrounds[J]. Remote Sensing, 11, 765(2019). http://www.researchgate.net/publication/332078787_a_sar_dataset_of_ship_detection_for_deep_learning_under_complex_backgrounds

    [30] Sun X, Wang Z R, Sun Y R et al. AIR-SARShip-1.0: high-resolution SAR ship detection dataset[J]. Journal of Radars, 8, 852-862(2019).

    [31] Du L, Liu B, Wang Y et al. Target detection method based on convolutional neural network for SAR image[J]. Journal of Electronics & Information Technology, 38, 3018-3025(2016).

    [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).

    [34] Yang L, Su J, Li X. Application of SAR ship data augmentation based on generative adversarial network in improved SSD[J]. Acta Armamentarii, 40, 2488-2496(2019).

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

    [36] Li J W, Qu C W, Peng S J et al. Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics & Information Technology, 41, 143-149(2019).

    [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).

    [38] Li S Y, Fu G Y, Cui Z M et al. Data augmentation in sar images based on multi-scale generative adversarial networks[J]. Laser & Optoelectronics Progress, 57, 201018(2020).

    [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).

    [40] Yang L, Su J, Huang H et al. SAR ship detection based on convolutional neural network with deep multiscale feature fusion[J]. Acta Optica Sinica, 40, 0215002(2020).

    [41] Dai W X, Mao Y Q, Yuan R et al. A novel detector based on convolution neural networks for multiscale SAR ship detection in complex background[J]. Sensors, 20, 2547(2020). http://www.researchgate.net/publication/341054628_A_Novel_Detector_Based_on_Convolution_Neural_Networks_for_Multiscale_SAR_Ship_Detection_in_Complex_Background

    [42] Miao K, Ke F J, Xiang G L et al. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection[J]. Remote Sensing, 9, 860(2017). http://adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2017RemS....9..860.&link_type=EJOURNAL&db_key=PHY&high=

    [43] Chen S Q, Zhan R H, Zhang J. Regional attention-based single shot detector for SAR ship detection[J]. Journal of Engineering, 2019, 7381-7384(2019). http://www.researchgate.net/publication/336809247_Regional_attention-based_single_shot_detector_for_SAR_ship_detection

    [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).

    [45] Gao F, Shi W, Wang J et al. Enhanced feature extraction for ship detection from multi-resolution and multi-scene synthetic aperture radar (SAR) images[J]. Remote Sensing, 11, 2694(2019). http://www.researchgate.net/publication/337360394_enhanced_feature_extraction_for_ship_detection_from_multi-resolution_and_multi-scene_synthetic_aperture_radar_sar_images

    [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).

    [48] Pan Z R, Yang R, Zhang A Z. MSR2N: multi-stage rotational region based network for arbitrary-oriented ship detection in SAR images[J]. Sensors, 20, 2340(2020). http://www.researchgate.net/publication/340842420_MSR2N_Multi-Stage_Rotational_Region_Based_Network_for_Arbitrary-Oriented_Ship_Detection_in_SAR_Images

    [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).

    [50] Zhang X H, Yao L, Lü Y F et al. Center based model for arbitrary-oriented ship detection in remote sensing images[J]. Acta Photonica Sinica, 49, 0410005(2020).

    [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..

    [52] Hu C H, Chen C, He C et al. SAR detection for small target ship based on deep convolutional neural network[J]. Journal of Chinese Inertial Technology, 27, 397-405, 414(2019).

    [53] Chen P, Li Y, Zhou H et al. Detection of small ship objects using anchor boxes cluster and feature pyramid network model for SAR imagery[J]. Journal of Marine Science and Engineering, 8, 112(2020). http://www.researchgate.net/publication/339228924_Detection_of_Small_Ship_Objects_Using_Anchor_Boxes_Cluster_and_Feature_Pyramid_Network_Model_for_SAR_Imagery

    [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).

    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
    Download Citation