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
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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

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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

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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

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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

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图 5 旋转图像示例

Figure 5. The examples of rotated images

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图 6 DCENN网络主要结构

Figure 6. The main structure of DCENN network

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图 7 基于密集连接融合特征图

Figure 7. The fusion feature map based on dense connection

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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

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1 AIR-SARShip-1.0数据集发布地址

1. Release address of AIR-SARShip-1.0 dataset

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图 1 Annotated example in the dataset

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图 2 Examples of the AIR-SARShip-1.0 dataset

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图 3 Area distribution of ship rectangle in the dataset

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图 4 Imaging examples of the same area at different angles

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图 5 Examples of original image and rotated images

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图 6 Main structure of the DCENN network

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图 7 Fusion feature map based on dense connection

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

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

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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

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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

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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

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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

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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

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表 1 The dataset information

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

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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

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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

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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

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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

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