AIR-SARShip-1.0：高分辨率SAR舰船检测数据集

孙显; 王智睿; 孙元睿; 刁文辉; 张跃; 付琨

doi:10.12000/JR19097

AIR-SARShip-1.0：高分辨率SAR舰船检测数据集

DOI: 10.12000/JR19097

孙显^{1, 2, 3, ,},
王智睿^{1, 3,},
孙元睿^{1, 2,},
刁文辉^{1, 3,},
张跃^{1, 3,},
付琨^{1, 2, 3,}

1.
中国科学院空天信息创新研究院北京 100190
2.
中国科学院大学北京 100190
3.
中国科学院网络信息体系技术科技创新重点实验室北京 100190

基金项目: 国家自然科学基金(61725105, 41801349, 41701508)，国家高分辨率对地观测系统重大专项（GFZX0404120405）

详细信息

作者简介:
孙　显(1981–)，男，中国科学院空天信息创新研究院研究员，博士生导师，主要研究方向为计算机视觉与遥感图像理解，IEEE高级会员，雷达学报青年编委。E-mail: sunxian@mail.ie.ac.cn

王智睿(1990–)，男，2018年在清华大学获得博士学位，现任中国科学院空天信息创新研究院助理研究员，主要研究方向为SAR图像智能解译。E-mail: zhirui1990@126.com

孙元睿(1995–)，男，博士生，2017年获得中国地质大学(武汉)工学学士学位，现为中国科学院大学信息与通信工程博士生，主要研究方向为SAR舰船检测。E-mail: sunyuanrui17@mails.ucas.ac.cn

刁文辉(1988–)，男，2016年在中国科学院大学获得博士学位，现任中国科学院空天信息创新研究院助理研究员。主要研究方向为深度学习理论及其在遥感图像解译中的应用，目前已发表论文20余篇。E-mail: whdiao@mail.ie.ac.cn

张　跃(1990–)，男， 2017年在中国科学院大学获得博士学位，现任中国科学院空天信息创新研究院助理研究员。主要研究方向为SAR图像智能分析与解译应用，目前已发表SCI论文10余篇。E-mail: zhangyue@air.cas.ac.cn

付　琨(1974–)，男，研究员，博士生导师，现任中国科学院空天信息创新研究院院长助理，中国科学院重点实验室主任，主要从事地理空间数据分析与挖掘、遥感图像智能解译等领域的研究工作，先后获国家科技进步特等奖、国家科技进步一等奖和省部级一等奖等多项。E-mail: fukun@mail.ie.ac.cn

通讯作者:
孙显 sunxian@mail.ie.ac.cn

中图分类号: TN957.51; TN958
计量
- 文章访问数: 22336
- HTML全文浏览量: 19209
- PDF下载量: 2681
- 被引次数: 0
出版历程
- 收稿日期: 2019-11-16
- 修回日期: 2019-12-17
- 网络出版日期: 2019-12-01

AIR-SARShip-1.0: High-resolution SAR Ship Detection Dataset (in English)

SUN Xian^{1, 2, 3
, ,},
WANG Zhirui^{1, 3
,},
SUN Yuanrui^{1, 2
,},
DIAO Wenhui^{1, 3
,},
ZHANG Yue^{1, 3
,},
FU Kun^{1, 2, 3
,}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100190, China
3.
Key Laboratory of Network Information System Technology(NIST), Aerospace InformationResearch Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (61725105, 41801349, 41701508), National Major Project on High Resolution Earth Observation System (GFZX0404120405)

More Information

Author Bio:
SUN Xian was born in 1981. He is a researcher and doctoral supervisor at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research fields are computer vision and remote sensing image interpretation. E-mail: sunxian@mail.ie.ac.cn

WANG Zhirui was born in 1990. He received his PhD from Tsinghua University in 2018. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research field is intelligent interpretation of SAR images. E-mail: zhirui1990@126.com

SUN Yuanrui was born in 1995. He received his bachelor’s degree in engineering from China University of Geosciences (Wuhan) in 2017. He is now a doctoral candidate in information and communication engineering of the University of Chinese Academy of Sciences. His main research field is SAR ship detection. E-mail: sunyuanrui17@mails.ucas.ac.cn

DIAO Wenhui was born in 1988. He received his PhD from the University of Chinese Academy of Sciences in 2016. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research interests include deep learning theory and its application in remote sensing image interpretation. E-mail: whdiao@mail.ie.ac.cn

ZHANG Yue was born in 1990. He received his PhD from the University of Chinese Academy of Sciences in 2017. He is currently a research assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences. His main research field is intelligent analysis and interpretation of SAR images. E-mail: zhangyue@air.cas.ac.cn

FU Kun was born in 1974. He is a researcher and doctoral supervisor. He is the president assistant at the Aerospace Information Research Institute, Chinese Academy of Sciences, and the director of the Key Laboratory of the Chinese Academy of Sciences. He is mainly engaged in research in the fields of geospatial data analysis and mining, and remote sensing image intelligent interpretation. He has successively won the National Science and Technology Progress Award, the first prize of the National Science and Technology Progress Award, and the first prize of the Provincial and Ministerial-Level Award. E-mail: fukun@mail.ie.ac.cn

Corresponding author: SUN Xian, sunxian@mail.ie.ac.cn

摘要

摘要:
近年来，深度学习技术得到广泛应用，然而在合成孔径雷达(SAR)舰船目标检测研究中，由于数据获取难、样本规模小，尚难以支撑深度网络模型的训练。该文公开了一个面向高分辨率、大尺寸场景的SAR舰船检测数据集，该数据集包含31景高分三号SAR图像，场景类型包含港口、岛礁、不同级别海况的海面等，背景涵盖近岸和远海等多样场景。同时，该文使用经典舰船检测算法和深度学习算法进行了实验，其中基于密集连接端到端网络方法效果最佳，平均精度达到88.1%。通过实验对比分析形成指标基准，方便其他学者在此数据集基础上进一步展开SAR舰船检测相关研究。
- SAR舰船检测 /
- 公开数据集 /
- 深度学习
Abstract:
Over the recent years, deep-learning technology has been widely used. However, in research based on Synthetic Aperture Radar (SAR) ship target detection, it is difficult to support the training of a deep-learning network model because of the difficulty in data acquisition and the small scale of the samples. This paper provides a SAR ship detection dataset with a high resolution and large-scale images. This dataset comprises 31 images from Gaofen-3 satellite SAR images, including harbors, islands, reefs, and the sea surface in different conditions. The backgrounds include various scenarios such as the near shore and open sea. We conducted experiments using both traditional detection algorithms and deep-learning algorithms and observed the densely connected end-to-end neural network to achieve the highest average precision of 88.1%. Based on the experiments and performance analysis, corresponding benchmarks are provided as a basis for further research on SAR ship detection using this dataset.
- SAR ship detection /
- Public dataset /
- Deep learning

HTML全文

图 1 数据集标注示意图

Figure 1. The annotated example in the dataset

下载: 全尺寸图片幻灯片

图 2 AIR-SARShip-1.0数据集中场景示例

Figure 2. The example scenes of AIR-SARShip-1.0 dataset

下载: 全尺寸图片幻灯片

图 3 数据集舰船矩形框面积分布

Figure 3. The area distribution of ship rectangle in the dataset

下载: 全尺寸图片幻灯片

图 4 同一地区不同角度成像示例

Figure 4. Imaging examples of the same area at different angles

下载: 全尺寸图片幻灯片

图 5 旋转图像示例

Figure 5. The examples of rotated images

下载: 全尺寸图片幻灯片

图 6 DCENN网络主要结构

Figure 6. The main structure of DCENN network

下载: 全尺寸图片幻灯片

图 7 基于密集连接融合特征图

Figure 7. The fusion feature map based on dense connection

下载: 全尺寸图片幻灯片

图 8 基于Faster-RCNN的SAR舰船检测示意图

Figure 8. The detection example of SAR ship based on Faster-RCNN

下载: 全尺寸图片幻灯片

1 AIR-SARShip-1.0数据集发布地址

1. Release address of AIR-SARShip-1.0 dataset

下载: 全尺寸图片幻灯片

图 1 Annotated example in the dataset

下载: 全尺寸图片幻灯片

图 2 Examples of the AIR-SARShip-1.0 dataset

下载: 全尺寸图片幻灯片

图 3 Area distribution of ship rectangle in the dataset

下载: 全尺寸图片幻灯片

图 4 Imaging examples of the same area at different angles

下载: 全尺寸图片幻灯片

图 5 Examples of original image and rotated images

下载: 全尺寸图片幻灯片

图 6 Main structure of the DCENN network

下载: 全尺寸图片幻灯片

图 7 Fusion feature map based on dense connection

下载: 全尺寸图片幻灯片

图 8 Detection example of SAR ship based on Faster-RCNN

下载: 全尺寸图片幻灯片

App Fig. 1 Release address of the AIR-SARShip-1.0 dataset

下载: 全尺寸图片幻灯片

表 1 数据集信息

Table 1. The dataset information

分辨率	成像模式	极化方式	图像格式
1 m, 3 m	聚束式、条带式	单极化	Tiff

下载: 导出CSV

1 AIR-SARShip-1.0数据集详情

1. AIR-SARShip-1.0 dataset information in detail

图像编号	像素尺寸	海况	场景	分辨率(m)	舰船数量
1	3000×3000	2级	近岸	3	5
2	3000×3000	0级	近岸	1	7
3	3000×3000	3级	远海	3	10
4	3000×3000	2级	远海	3	8
5	3000×3000	1级	近岸	3	15
6	3000×3000	4级	远海	3	3
7	3000×3000	4级	远海	3	5
8	3000×3000	1级	近岸	1	2
9	3000×3000	2级	近岸	1	7
10	3000×3000	1级	远海	1	50
11	3000×3000	1级	近岸	1	80
12	3000×3000	2级	近岸	1	18
13	4140×4140	1级	近岸	1	21
14	3000×3000	1级	近岸	1	15
15	3000×3000	1级	近岸	1	77
16	3000×3000	3级	近岸	3	13
17	3000×3000	3级	近岸	3	3
18	3000×3000	3级	近岸	3	2
19	3000×3000	3级	近岸	3	1
20	3000×3000	2级	近岸	3	7
21	3000×3000	2级	近岸	3	9
22	3000×3000	1级	近岸	3	14
23	3000×3000	1级	远海	3	4
24	3000×3000	4级	远海	3	6
25	3000×3000	4级	远海	1	20
26	3000×3000	2级	近岸	3	15
27	3000×3000	2级	近岸	3	19
28	3000×3000	1级	近岸	3	8
29	3000×3000	3级	远海	3	6
30	3000×3000	2级	远海	3	8
31	3000×3000	1级	近岸	3	3

下载: 导出CSV

表 2 经典机器学习算法舰船检测性能基准

Table 2. The performance benchmarks of classic ship detection algorithm

算法	AP(%)
CFAR	27.1
基于K分布的CFAR	19.2
KSW	28.2

下载: 导出CSV

表 3 基于深度学习的SAR舰船检测算法的性能基准

Table 3. The performance benchmarks of SAR ship detection algorithms based on deep learning

性能排名	算法	AP(%)	FPS
1	DCENN	88.1	24
2	Faster-RCNN-DR	84.2	29
3	Faster-RCNN	79.3	30
4	SSD-512	74.3	64
5	SSD-300	72.4	151
6	YOLOv1	64.7	160

下载: 导出CSV

表 4 不同场景下算法性能结果

Table 4. The performance benchmarks of different scenes based on different algorithms

性能排名	算法	近岸舰船AP(%)	远海舰船AP(%)
1	DCENN	68.1	96.3
2	Faster-RCNN-DR	57.6	94.6
3	SSD-512	40.3	89.4

下载: 导出CSV

表 1 The dataset information

Resolution	Imaging mode	Polarization mode	Format
1 m, 3 m	Spotlight, Strip	Single	Tiff

下载: 导出CSV

表 2 The performance benchmarks of classic ship detection algorithm

Algorithm	AP(%)
CFAR	27.1
CFAR method based on K distribution	19.2
KSW	28.2

下载: 导出CSV

表 3 The performance benchmarks of SAR ship detection algorithms based on deep learning

Performance ranking	Algorithm	AP(%)	FPS
1	DCENN	88.1	24
2	Faster-RCNN-DR	84.2	29
3	Faster-RCNN	79.3	30
4	SSD-512	74.3	64
5	SSD-300	72.4	151
6	YOLOv1	64.7	160

下载: 导出CSV

表 4 The performance benchmarks of different scenes based on different algorithms

Performance ranking	Algorithm	Nearshore ship AP(%)	Offshore ship AP(%)
1	DCENN	68.1	96.3
2	Faster-RCNN-DR	57.6	94.6
3	SSD-512	40.3	89.4

下载: 导出CSV

App Tab. 1 AIR-SARShip-1.0 dataset information in detail

Image No.	Size	Sea condition	Scenario	Resolution (m)	Ship numbe
1	3000×3000	Level 2	nearshore	3	5
2	3000×3000	Level 0	nearshore	1	7
3	3000×3000	Level 3	offshore	3	10
4	3000×3000	Level 2	offshore	3	8
5	3000×3000	Level 1	nearshore	3	15
6	3000×3000	Level 4	offshore	3	3
7	3000×3000	Level 4	offshore	3	5
8	3000×3000	Level 1	nearshore	1	2
9	3000×3000	Level 2	nearshore	1	7
10	3000×3000	Level 1	offshore	1	50
11	3000×3000	Level 1	nearshore	1	80
12	3000×3000	Level 2	nearshore	1	18
13	4140×4140	Level 1	nearshore	1	21
14	3000×3000	Level 1	nearshore	1	15
15	3000×3000	Level 1	nearshore	1	77
16	3000×3000	Level 3	nearshore	3	13
17	3000×3000	Level 3	nearshore	3	3
18	3000×3000	Level 3	nearshore	3	2
19	3000×3000	Level 3	nearshore	3	1
20	3000×3000	Level 2	nearshore	3	7
21	3000×3000	Level 2	nearshore	3	9
22	3000×3000	Level 1	nearshore	3	14
23	3000×3000	Level 1	offshore	3	4
24	3000×3000	Level 4	offshore	3	6
25	3000×3000	Level 4	offshore	1	20
26	3000×3000	Level 2	nearshore	3	15
27	3000×3000	Level 2	nearshore	3	19
28	3000×3000	Level 1	nearshore	3	8
29	3000×3000	Level 3	offshore	3	6
30	3000×3000	Level 2	offshore	3	8
31	3000×3000	Level 1	nearshore	3	3

下载: 导出CSV

参考文献(25)

[1]	张杰, 张晰, 范陈清, 等. 极化SAR在海洋探测中的应用与探讨[J]. 雷达学报, 2016, 5(6): 596–606. doi: 10.12000/JR16124 ZHANG Jie, ZHANG Xi, FAN Chenqing, et al. Discussion on application of polarimetric synthetic aperture radar in marine surveillance[J]. Journal of Radars, 2016, 5(6): 596–606. doi: 10.12000/JR16124
[2]	REY M T, CAMPBELL J, and PETROVIC D. A comparison of ocean clutter distribution estimators for CFAR-based ship detection in RADARSAT imagery[R]. Technical Report No. 1340, 1998.
[3]	NOVAK L M, BURL M C, and IRVING W W. Optimal polarimetric processing for enhanced target detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 234–244. doi: 10.1109/7.249129
[4]	STAGLIANO D, LUPIDI A, and BERIZZI F. Ship detection from SAR images based on CFAR and wavelet transform[C]. 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing, Naples, Italy, 2012: 53–58.
[5]	HE Jinglu, WANG Yinghua, LIU Hongwei, et al. A novel automatic PolSAR ship detection method based on superpixel-level local information measurement[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(3): 384–388. doi: 10.1109/LGRS.2017.2789204
[6]	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.
[7]	EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The PASCAL Visual Object Classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303–338. doi: 10.1007/s11263-009-0275-4
[8]	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco: Common objects in context[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 740–755.
[9]	XIA Guisong, BAI Xiang, DING Jian, et al. DOTA: A large-scale dataset for object detection in aerial images[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3974–3983.
[10]	ZHANG Yuanlin, YUAN Yuan, FENG Yachuang, et al. Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5535–5548. doi: 10.1109/TGRS.2019.2900302
[11]	LONG Yang, GONG Yiping, XIAO Zhifeng, et al. Accurate object localization in remote sensing images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(5): 2486–2498. doi: 10.1109/TGRS.2016.2645610
[12]	XIAO Zhifeng, LIU Qing, TANG Gefu, et al. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images[J]. International Journal of Remote Sensing, 2015, 36(2): 618–644. doi: 10.1080/01431161.2014.999881
[13]	LI Jianwei, QU Changwen, and SHAO Jiaqi. Ship detection in SAR images based on an improved faster R-CNN[C]. 2017 SAR in Big Data Era: Models, Beijing, China, 2017: 1–6.
[14]	HUANG Lanqing, LIU Bin, LI Boying, et al. OpenSARShip: A dataset dedicated to Sentinel-1 ship interpretation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 195–208. doi: 10.1109/JSTARS.2017.2755672
[15]	WANG Yuanyuan, WANG Chao, ZHANG Hong, 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
[16]	张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049 ZHANG Qingjun. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049
[17]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
[18]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788.
[19]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. 2017 IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007.
[20]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
[21]	GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.
[22]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]. The 28th International Conference on Neural Information Processing Systems, Montreal, Canada, 2015: 91–99.
[23]	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, Honolulu, USA, 2017: 936–944.
[24]	LIU Peng and JIN Yaqiu. A study of ship rotation effects on SAR image[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3132–3144. doi: 10.1109/TGRS.2017.2662038
[25]	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