Ship target detection of SAR images in complex scenes

doi:10.16411/j.cnki.issn1006-7736.2020.03.010

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Journal of Dalian Maritime University ›› 2020, Vol. 46 ›› Issue (3) : 87-94. DOI: 10.16411/j.cnki.issn1006-7736.2020.03.010

ZHOU Hui¹， CHU Na¹，CHEN Peng²

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Abstract

Aiming at the problem of ship target detection in complex scene of SAR image, a Mask-FPN model combining localization, classification and segmentation multitasking was proposed. Based on pyramid feature map, the image segmentation branches were introduced at the same time, and multi task loss function was used to ensure that the three processes as positioning, classification and segmentation were carried out simultaneously. The experimental results show that the accuracy of the Mask-FNP model is up to 98.81% in the complex scenes with interference background such as offshore, port and island. Compared with Faster R-CNN, Yolo3, SSD, FPN and other models, the detection accuracy of this model is higher, and the false alarm rate and missing detection rate are significantly reduced.

Key words

synthetic aperture radar(SAR) images / multi-target ship detection / complex scenes / feature pyramid model / image segmentation / multitask loss function

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ZHOU Hui，CHU Na，CHEN Peng. Ship target detection of SAR images in complex scenes[J]. Journal of Dalian Maritime University. 2020, 46(3): 87-94 https://doi.org/10.16411/j.cnki.issn1006-7736.2020.03.010

References

[1] Eldhuset, K. An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1996, 34, 1010–1019.
[2] Brekke, C.; Solberg, A. Oil spill detection by satellite remote sensing. REMOTE SENSING OF ENVIRONMENT 2005, 95, 1–13.
[3] Huang, Z.; Pan, Z.; Lei, B. Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sensing 2017, 9, 907.
[4] Wang, C.; Zhang, H.; Wu, F.; Jiang, S.; Zhang, B.; Tang, Y. A Novel Hierarchical Ship Classifier for COSMO-SkyMed SAR Data. IEEE Geoscience and Remote Sensing Letters 2014, 11, 484–488.
[5] Cheng, G.; Zhou, P.; Han, J. Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2016, 54, 7405–7415.
[6] Zhu C , Zhou H , Wang R , et al. A Novel Hierarchical Method of Ship Detection from Spaceborne Optical Image Based on Shape and Texture Features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(9):3446-3456.
[7] Shi Z , Yu X , Jiang Z , et al. Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8):4511-4523.
[8] Zhang W , Sun X , Wang H , et al. A generic discriminative part-based model for geospatial object detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 99:30-44.
[9] Huang, Z.; Pan, Z.; Lei, B. Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data. Remote Sensing 2017, 9, 907.
[10] Liu Y, Zhang M H , Xu P , et al. SAR ship detection using sea-land segmentation-based convolutional neural network[C].2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP). IEEE, 2017:1–4.
[11] Kang M , Ji K , Leng X , et al. Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection[J]. Remote Sensing, 2017, 9(8):860–873.
[12] 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 (RSIP). IEEE, 2017:12-15.
[13] Wang Y Y, Wang C, Zhang H. Combining a single shot multibox detector with transfer learning for ship detection using sentinel-1 SAR images[J]. Remote Sensing Letters, 2018, 9(8):780–788.
[14] LI J J, 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 and Information Technology, 2019: 143-149.
[15] WEI S J, JIANG P F, YUAN Q Z, et al Detection and recognition of sar small ship objects using deep neural network[J]. Journal of Northwestern Polytechnical University, 2019, 37(03): 587-593.
[16] HU C H, CHEN C, HE C, et al. Detection and recognition of sar small ship objects using deep neural network[J]. Journal of Chinese Inertial Technology, 2019(03): 397- 405+414
[17] Lin T Y, Dollar P, Girshick R, et al. Feature Pyramid Networks for Object Detection[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 2017: 2117–2125.
[18] Zhao, Q.; Sheng, T.; Wang, Y.; Tang, Z.; Chen, Y.; Cai, L.; Ling, H. M2det: A single-shot object detector based on multi-level feature pyramid network. In Proceedings of the Proceedings of the AAAI Conference on Artificial Intelligence; 2019; Vol. 33, pp. 9259–9266.
[19] Ghiasi, G.; Lin, T.-Y.; Le, Q.V. Nas-fpn: Learning scalable feature pyramid architecture for object detection. In Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2019; pp. 7036–7045.
[20] Huang J, Jiang Z G, Zhang H P, et al. Remote Sensing Image Ship Target Detection on Based on Convolutional Neural Network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2017, 43(9):1841-1848. (in Chinese)
[21] Ji K, Xing X, Chen W, et al. Ship classification in TerraSAR-X SAR images based on classifier combination[C]. IEEE International Geoscience & Remote Sensing Symposium-IGarss. IEEE, 2014:1562–1566.
[22] Sun W, Wang R. Fully Convolutional Networks for Semantic Segmentation of Very High Resolution Remotely Sensed Images Combined With DSM[J]. IEEE Geoscience and Remote Sensing Letters, 2018(99):1-5.
Chen L C, Papandreou G, Kokkinos I , et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 40(4):