基于语义分割实现的SAR图像舰船目标检测

doi:10.12305/j.issn.1001-506X.2022.04.16

Abstract

Abstract:

Object detection methods based on deep learning have achieved outstanding success in natural images, and the application of many methods to ship detection in synthetic aperture radar (SAR) images has gradually become a new trend. How to improve the existing methods and combine them with the characteristics of SAR images to complete specific detection tasks has become the main research direction at present. Different from the current detection methods, this paper rethinks the existing deep learning-based SAR image ship detection algorithms and proposes an integrated detection and segmentation method based on semantic segmentation. This detection method realized by semantic segmentation can effectively avoid the complicated decoding process of many detection networks. Besides, it has the characteristics of predicted bounding boxes generated in the prediction stage that are more suitable for targets, and has a higher precision and recall rate compared with other methods. Although this method belongs to the anchor-free detection field, the experimental results show it achieves a two-stage detection effect, and has more refined segmentation results, which is suitable for complicated background noise-based detection and segmentation.

Key words: synthetic aperture radar (SAR), semantic segmentation, ship detection, convolutional neural network (CNN), deep learning

CLC Number:

TP751

Dong CHEN, Yanwei JU. Ship object detection SAR images based on semantic segmentation[J]. Systems Engineering and Electronics, 2022, 44(4): 1195-1201.

Figures/Tables 8

Fig.1

Fig.2

Table 1

Table 2

Table 3

Fig.3

Fig.4

Fig.5

References 41

1	VILLANO M , KRIEGER G , STEINBRECHER U , et al. Simultaneous single-/dual- and quad-pol SAR imaging over swaths of different widths[J]. IEEE Trans. on Geoscience and Remote Sensing, 2020, 25 (3): 2096- 2103.
2	焦李成, 张向荣, 侯彪, 等. 智能SAR图像处理与解译[M]. 北京: 科学出版社, 2008.
	JIAO L C , ZHANG X R , HOU B , et al. Intelligent SAR image processing and interpretation[M]. Beijing: China Science Publishing & Media Ltd., 2008.
3	SHEN H F , ZHOU C X , LI J , et al. SAR image despeckling employing a recursive deep CNN prior[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 59 (1): 273- 286. doi: 10.1109/TGRS.2020.2993319
4	WANG P Y , ZHANG H , PATEL V M . SAR image despeckling using a convolutional neural network[J]. IEEE Signal Processing Letters, 2017, 24 (12): 1763- 1767. doi: 10.1109/LSP.2017.2758203
5	CUI Z Y , WANG X Y , LIU N Y , et al. ship detection in large-scale SAR images via spatial shuffle-group enhance attention[J]. IEEE Trans. on Geoscience and Remote Sensing, 2021, 59 (1): 379- 391. doi: 10.1109/TGRS.2020.2997200
6	LIU L, CHEN G W, PAN Z X, et al. Inshore ship detection in SAR images based on deep neural networks[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2018: 5-28.
7	陈冬, 句彦伟. 基于改进型YOLOv3的SAR图像舰船目标检测[J]. 系统工程与电子技术, 2021, 43 (4): 937- 943.
	CHEN D , JU Y W . SAR ship detection based on improved YOLOv3[J]. Systems Engineering and Electronics, 2021, 43 (4): 937- 943.
8	LI W K, ZOU B, XIN Y, et al. An improved CFAR scheme for man-made target detection in high resolution SAR images[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2018: 2829-2832.
9	杨国铮, 禹晶, 肖创柏, 等. 基于形态字典学习的复杂背景SAR图像舰船尾迹检测[J]. 自动化学报, 2017, 43 (10): 1713- 1725.
	YANG G Z , YU J , XIAO C B , et al. Ship wake detection in SAR images with complex background using morphological dictionary learning[J]. Acta Automatic Sinica, 2017, 43 (10): 1713- 1725.
10	ZHU J W , QIU X L , PAN Z X , et al. Projection shape template-based ship target recognition in TerraSAR-X images[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14 (2): 222- 226. doi: 10.1109/LGRS.2016.2635699
11	CHEN G , LI G , LIU Y , et al. SAR image despeckling based on combination of fractional-order total variation and nonlocal low rank regularization[J]. IEEE Trans. on Geoscience and Remote Sensing, 2020, 58 (3): 2056- 2070. doi: 10.1109/TGRS.2019.2952662
12	MOLINI A B, VALSESIA D, FRACASTORO G, et al. Towards deep unsupervised SAR despeckling with blind-spot convolutional neural networks[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2020: 2507-2510.
13	DALALSASSO E , DENIS L , TUPIN F . SAR2SAR: a semi-supervised despeckling algorithm for SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14, 4321- 4329. doi: 10.1109/JSTARS.2021.3071864
14	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proc. of the Computer Vision and Pattern Recognition, 2014: 580-587.
15	GIRSHICK R. Fast R-CNN[C]//Proc. of the International Conference on Computer Vision, 2015: 1440-1448.
16	REN S Q , HE K M , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans. on Pattern Analysis & Machine Intelligence, 2017, 39 (6): 1137- 1149.
17	CAI Z, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]//Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.
18	HE K , GKIOXARI G , DOLLÁR P , et al. Mask R-CNN[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2020, 42 (2): 386- 397. doi: 10.1109/TPAMI.2018.2844175
19	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proc. of the European Conference on Computer Vision, 2016: 21-37.
20	LI Z X , ZHOU F Q . FSSD: feature fusion single shot multibox detector[J]. Computer Vision and Pattern Recognition, 2018, 36 (7): 356- 366.
21	REDMON J, DIVVALA S K, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proc. of the Computer Vision and Pattern Recognition, 2016: 779-788.
22	REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proc. of the Computer Vision and Pattern Recognition, 2017: 6517-6525.
23	REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. [2021-5-10]. https://arxiv.org/abs/1804.02767v1.
24	BOCHKOVSKIY A, WANG C, LIAO H. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. [2021-5-10]. https://arxiv.org/abs/2004.10934v1.
25	LI R , WANG X D , WANG J , et al. SAR target recognition based on efficient fully convolutional attention block CNN[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 4005905.
26	WU S N, WANG K, OUYANG Y W. Study on small samples SAR image recognition detection method based on transfer CNN[C]//Proc. of the 3rd International Conference on Electronic Information Technology and Computer Engineering, 2019: 718-722.
27	SNELL J, SWERSKY K, ZEMEL R. Prototypical networks for few-shot learning[C]//Proc. of the Neural Information Processing Systems, 2017: 4077-4087.
28	HUANG H H, ZHANG F, ZHOU Y S, et al. High resolution SAR image synthesis with hierarchical generative adversarial networks[C]//Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2019: 2782-2785.
29	LI J W, QU C W, SHAO J Q. Ship detection in SAR images based on an improved faster R-CNN[C]//Proc. of the SAR in Big Data Era: Models, Methods and Applications, 2017.
30	TIAN Z, SHEN C H, CHEN H, et al. FCOS: fully convolutional one-stage object detection[C]//Proc. of the IEEE/CVF International Conference on Computer Vision, 2019: 9626-9635.
31	ZHOU X Y, WANG D Q, KRÄHENBÜHL P. Objects as points[EB/OL]. [2021-5-10]. https://arxiv.org/abs/1904.07850v2.
32	NICOLAS C, FRANCISCO M, GABRIEL S, et al. End-to-end object detection with transformers[C]//Proc. of the European Conference on Computer Vision, 2020: 213-229.
33	ASHISH V, NOAM S, NIKI P, et al. Attention is all you need[C]// Proc. of the 31st International Conference on Neural Information Processing Systems, 2017: 6000-6010.
34	WANG Y Y , WANG C , ZHANG H , et al. A SAR dataset of ship detection for deep learning under complex backgrounds[J]. Remote Sensing, 2019, 11 (7): 765. doi: 10.3390/rs11070765
35	WEI S J , ZENG X F , QU Q Z , et al. HRSID: a high-resolution SAR images dataset for ship detection and instance segmentation[J]. IEEE Access, 2020, 8, 120234- 120254. doi: 10.1109/ACCESS.2020.3005861
36	MINAEE S , BOYKOV Y Y , PORIKLI F , et al. Image segmentation using deep learning: a survey[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2021, doi: 10.1109/TPAMI.2021.3059968
37	SHELHAMER E , LONG J , DARRELL T . Fully convolutional networks for semantic segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017, 39 (4): 640- 651. doi: 10.1109/TPAMI.2016.2572683
38	RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedicalimage segmentation[C]//Proc. of the Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.
39	BADRINARAYANAN V , KENDALL A , CIPOLLA R . SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2017, 39 (12): 2481- 2495. doi: 10.1109/TPAMI.2016.2644615
40	CHEN L , PAPANDREOU G , KOKKINOS I , et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2018, 40 (4): 834- 848. doi: 10.1109/TPAMI.2017.2699184
41	MILLETARI F, NAVAB N, AHMADI S. V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]//Proc. of the 4th International Conference on 3D Vision, 2016: 565-571.

类别	CE	Dice	MIoU	MPA
背景	✔	✘	99.85	99.92
背景	✔	✔	99.84	99.89
舰船目标	✔	✘	67.91	81.61
舰船目标	✔	✔	69.05	88.91
总计	✔	✘	83.88	90.76
总计	✔	✔	84.45	94.40

模型	Dice	AP	AP_0.5	AP_0.75	AP_s	AP_m	AP_l
Faster R-CNN	-	0.529	0.806	0.590	0.541	0.570	0.215
YOLOv4	-	0.376	0.788	0.324	0.390	0.367	0.088
UNet-S	✘	0.460	0.660	0.516	0.458	0.503	0.232
UNet-S	✔	0.544	0.756	0.607	0.547	0.560	0.319

模型	Dice	Size/MB	AR	AR_s	AR_m	AR_l
Faster R-CNN	-	-	0.595	0.581	0.716	0.521
YOLOv4	-	244.4	0.438	0.434	0.495	0.233
UNet-S	✘	94.9	0.643	0.640	0.683	0.517
UNet-S	✔	94.9	0.688	0.683	0.744	0.566

[1]	Xiao HAN, Shiwen CHEN, Meng CHEN, Jincheng YANG. Open-set recognition of LPI radar signal based on reciprocal point learning [J]. Systems Engineering and Electronics, 2022, 44(9): 2752-2759.
[2]	Tian MIAO, Hongcheng ZENG, He WANG, Jie CHEN. A fast extraction method of flood areas based on iterative threshold segmentation using spaceborne SAR data [J]. Systems Engineering and Electronics, 2022, 44(9): 2760-2768.
[3]	Tao ZHANG, Xiaogang YANG, Ruitao LU, Xueli XIE, Chuang LIU. Key-point based method for ship detection in remote sensing images [J]. Systems Engineering and Electronics, 2022, 44(8): 2437-2447.
[4]	Caiyun WANG, Yida WU, Jianing WANG, Lu MA, Huanyue ZHAO. SAR image target recognition based on combinatorial optimization convolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(8): 2483-2487.
[5]	Limin ZHANG, Kaiwen TAN, Wenjun YAN, Yuyuan ZHANG. Radar emitter recognition based on multi-level jumper residual network [J]. Systems Engineering and Electronics, 2022, 44(7): 2148-2156.
[6]	Guodong JIN, Yuanliang XUE, Lining TAN, Jiankun XU. Advances in object tracking algorithm based on siamese network [J]. Systems Engineering and Electronics, 2022, 44(6): 1805-1822.
[7]	Dongning FU, Guisheng LIAO, Yan HUANG, Bangjie ZHANG, Xing WANG. Time-varying narrow-band interference suppression algorithm for SAR based on graph Laplacian embedding [J]. Systems Engineering and Electronics, 2022, 44(6): 1846-1853.
[8]	Minghui GAI, Su ZHANG, Weitian SUN, Yude NI, Lei YANG. Structural-feature enhancement of SAR targets based on complex value compatible total variation [J]. Systems Engineering and Electronics, 2022, 44(6): 1862-1872.
[9]	Xiaofeng ZHAO, Yebin XU, Fei WU, Jiahui NIU, Wei CAI, Zhili ZHANG. Ground infrared target detection method based on global sensing mechanism [J]. Systems Engineering and Electronics, 2022, 44(5): 1461-1467.
[10]	Penghui JI, Dahai DAI, Shiqi XING, Dejun FENG. Dense false moving targets generation method [J]. Systems Engineering and Electronics, 2022, 44(5): 1502-1511.
[11]	Hong ZOU, Chenyang BAI, Peng HE, Yaping CUI, Ruyan WANG, Dapeng WU. Edge service placement strategy based on distributed deep learning [J]. Systems Engineering and Electronics, 2022, 44(5): 1728-1737.
[12]	Jingming SUN, Shengkang YU, Jun SUN. Pose sensitivity analysis of HRRP recognition based on deep learning [J]. Systems Engineering and Electronics, 2022, 44(3): 802-807.
[13]	Lei YANG, Su ZHANG, Minghui GAI, Cheng FANG. High-resolution SAR imagery with enhancement of directional structure feature [J]. Systems Engineering and Electronics, 2022, 44(3): 808-818.
[14]	Hengyan LIU, Limin ZHANG, Wenjun YAN, Zhaogen ZHONG, Qing LING, Xiaojun LIANG. LDPC decoding based on WBP-CNN algorithm [J]. Systems Engineering and Electronics, 2022, 44(3): 1030-1035.
[15]	Kai SHAO, Miaomiao ZHU, Guangyu WANG. Modulation recognition method based on generative adversarial andconvolutional neural network [J]. Systems Engineering and Electronics, 2022, 44(3): 1036-1043.

Ship object detection SAR images based on semantic segmentation

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 41

Related Articles 15

Recommended Articles

Metrics

Comments