SAR-AIRcraft-1.0：高分辨率SAR飞机检测识别数据集

王智睿; 康玉卓; 曾璇; 汪越雷; 张汀; 孙显

doi:10.12000/JR23043

SAR-AIRcraft-1.0：高分辨率SAR飞机检测识别数据集

doi: 10.12000/JR23043

王智睿^{1, 4},
康玉卓^{1, 2, 3},
曾璇^{1, 2, 3},
汪越雷^{1, 2, 3},
张汀^{1, 2, 3},
孙显^{1, 2, 3, 4, ,}

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院大学北京 100049
3.
中国科学院大学电子电气与通信工程学院北京 100049
4.
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(62076241, 62171436)

详细信息

作者简介:
王智睿，博士，副研究员，主要研究方向为SAR图像智能解译、多源协同感知

康玉卓，博士生，主要研究方向为SAR图像智能解译

曾　璇，博士生，主要研究方向为SAR图像智能解译、极化SAR地物要素分类等

汪越雷，博士生，计算机视觉和模式识别、遥感图像解译

张　汀，博士生，主要研究方向为计算机视觉和模式识别、遥感图像解译

孙　显，博士，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

通讯作者:
孙显 sunxian@aircas.ac.cn

责任主编：徐丰 Corresponding Editor: XU Feng
中图分类号: TP753
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- 被引次数: 0
出版历程
- 收稿日期: 2023-04-17
- 修回日期: 2023-06-27
- 网络出版日期: 2023-07-17
- 刊出日期: 2023-08-28

SAR-AIRcraft-1.0: High-resolution SAR Aircraft Detection and Recognition Dataset

WANG Zhirui^{1, 4},
KANG Yuzhuo^{1, 2, 3},
ZENG Xuan^{1, 2, 3},
WANG Yuelei^{1, 2, 3},
ZHANG Ting^{1, 2, 3},
SUN Xian^{1, 2, 3, 4
, ,}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
Key Laboratory of Network Information System Technology (NIST), Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (62076241, 62171436)

More Information

Corresponding author: SUN Xian, sunxian@aircas.ac.cn

摘要

摘要: 针对合成孔径雷达(SAR)图像中飞机散射点离散以及背景强干扰造成虚警的问题，该文提出了一种结合散射感知的SAR飞机检测识别方法。一方面，通过上下文引导的特征金字塔模块来增强全局信息，减弱复杂场景中强干扰的影响，提高检测识别的准确率。另一方面，利用散射关键点对目标进行定位，设计散射感知检测模块实现对回归框的细化校正，增强目标的定位精度。为了验证方法有效性、同时促进SAR飞机检测识别领域的研究发展，该文制作并公开了一个高分辨率SAR-AIRcraft-1.0数据集。该数据集图像来自高分三号卫星，包含4,368张图片和16,463个飞机目标实例，涵盖A220, A320/321, A330, ARJ21, Boeing737, Boeing787和other共7个类别。该文将提出的方法和常见深度学习算法在构建的数据集上进行实验，实验结果证明了散射感知方法的优异性能，并且形成了该数据集在SAR飞机检测、细粒度识别、检测识别一体化等不同任务中性能指标的基准。
- 合成孔径雷达 /
- 公开数据集 /
- SAR飞机检测 /
- 飞机识别 /
- 深度学习
Abstract: This study proposes a Synthetic Aperture Radar (SAR) aircraft detection and recognition method combined with scattering perception to address the problem of target discreteness and false alarms caused by strong background interference in SAR images. The global information is enhanced through a context-guided feature pyramid module, which suppresses strong disturbances in complex images and improves the accuracy of detection and recognition. Additionally, scatter key points are used to locate targets, and a scatter-aware detection module is designed to realize the fine correction of the regression boxes to improve target localization accuracy. This study generates and presents a high-resolution SAR-AIRcraft-1.0 dataset to verify the effectiveness of the proposed method and promote the research on SAR aircraft detection and recognition. The images in this dataset are obtained from the satellite Gaofen-3, which contains 4,368 images and 16,463 aircraft instances, covering seven aircraft categories, namely A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and other. We apply the proposed method and common deep learning algorithms to the constructed dataset. The experimental results demonstrate the excellent effectiveness of our method combined with scattering perception. Furthermore, we establish benchmarks for the performance indicators of the dataset in different tasks such as SAR aircraft detection, recognition, and integrated detection and recognition.
- Synthetic Aperture Radar (SAR) /
- Public dataset /
- SAR aircraft detection /
- Aircraft recognition /
- Deep learning

HTML全文

图 1 SAR飞机检测识别中的挑战

Figure 1. The challenges in SAR aircraft detection and recognition

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图 2 不同类别SAR飞机和光学飞机样本示例

Figure 2. SAR and optical aircrafts of different categories

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图 3 各个类别的实例数量

Figure 3. The quantity of each type of instances

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图 4 飞机目标的尺寸分布

Figure 4. The size distribution of aircraft targets

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图 5 数据集标注示意图

Figure 5. The annotated results in the dataset

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图 6 提出方法的整体结构

Figure 6. The overall structure of the proposed method

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图 7 上下文引导的特征金字塔网络结构

Figure 7. The framework of context-guided feature pyramid network

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图 8 散射感知检测头的结构

Figure 8. The structure of scattering-aware detection head

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图 9 可视化结果展示

Figure 9. The visualization results

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图 10 混淆矩阵示意图

Figure 10. The confusion matrices for the methods

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图 11 不同先进方法的F1曲线

Figure 11. F1 curves of different advanced methods

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图 12 不同模块的F1曲线

Figure 12. F1 curves of different improvements in the proposed method

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图 13 不同模块的PR曲线

Figure 13. PR curves of different improvements in the proposed method

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图 14 检测结果和可视化

Figure 14. Detection results and visualization

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图 15 SA-Net的检测结果

Figure 15. Detection results of SA-Net

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1 SAR-AIRcraft-1.0：高分辨率SAR飞机检测识别数据集发布网页

1. Release webpage of SAR-AIRcraft-1.0: High-resolution SAR aircraft detection and recognition dataset

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表 1 SAR-AIRcraft-1.0数据集与其他SAR目标检测识别数据集的比较

Table 1. Comparison between the SAR-AIRcraft-1.0 dataset and other SAR object detection datasets

名称	类别	实例数量	图片数量	大小	发布年份	任务
MSTAR	10	5,950	5,950	128×128	1998	车辆识别
OpenSARShip	17	11,346	11,346	256×256	2017	舰船检测识别
SSDD	1	2,456	1,160	190~668	2017	舰船检测
SAR-Ship-Dataset	1	59,535	43,819	256×256	2019	舰船检测
AIR-SARShip-1.0	1	461	31	3000×3000	2019	舰船检测
HRSID	1	16,951	5,604	800×800	2020	舰船检测和实例分割
FUSAR-Ship	15	16,144	16,144	512×512	2020	舰船检测识别
SADD	1	7,835	2,966	224×224	2022	飞机检测
MSAR-1.0	4	60,396	28,449	256~2048	2022	飞机、油罐、桥梁、舰船检测
SAR-AIRcraft-1.0	7	16,463	4,368	800~1500	2023	飞机检测识别

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表 2 不同方法的检测结果(%)

Table 2. The detection results of different methods (%)

检测方法	P	R	F1	AP_0.5	AP_0.75
Faster R-CNN	77.6	78.1	77.8	71.6	53.6
Cascade R-CNN	89.0	79.5	84.0	77.8	59.1
Reppoints	62.7	88.7	81.2	80.3	52.9
SKG-Net	57.6	88.8	69.9	79.8	51.0
SA-Net	87.5	82.2	84.8	80.4	61.4

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表 3 不同类别实例目标的数量

Table 3. The number of instance targets of different categories

类别	训练集样本数量	测试集样本数量	总计
A330	278	31	309
A320/321	1719	52	1771
A220	3270	460	3730
ARJ21	825	362	1187
Boeing737	2007	550	2557
Boeing787	2191	454	2645
other	3223	1041	4264
总计	13513	2950	16463

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表 4 细粒度识别结果(%)

Table 4. Fine-grained recognition results (%)

方法	Acc (top-1/top-3)	A330	A320/321	A220	ARJ21	Boeing737	Boeing787	other
ResNet-50	75.59/89.19	74.19	90.38	78.04	73.76	61.64	78.63	80.50
ResNet-101	78.58/90.37	93.55	98.08	76.96	73.76	71.82	74.67	84.82
ResNeXt-50	80.61/89.46	83.87	94.23	78.91	74.86	73.27	83.04	85.40
ResNeXt-101	82.20/91.83	87.10	100	80.87	79.83	71.09	83.92	87.70
Swin Transformer	81.29/92.51	77.42	100	80.87	74.59	73.82	86.12	84.82

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表 5 基于深度学习算法的检测结果(IoU=0.5)

Table 5. The performance of the algorithms based on deep learning (IoU=0.5)

类别	Faster R-CNN	Cascade R-CNN	Reppoints	SKG-Net	SA-Net
A330	85.0	87.4	89.8	79.3	88.6
A320/321	97.2	97.5	97.9	78.2	94.3
A220	78.5	74.0	71.4	66.4	80.3
ARJ21	74.0	78.0	73.0	65.0	78.6
Boeing737	55.1	54.5	55.7	65.1	59.7
Boeing787	72.9	68.3	51.8	69.6	70.8
other	70.1	69.1	68.4	71.4	71.3
平均值(mAP)	76.1	75.7	72.6	70.7	77.7

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表 6 基于深度学习算法的检测结果(IoU=0.75)

Table 6. The performance of the algorithms based on deep learning (IoU=0.75)

类别	Faster R-CNN	Cascade R-CNN	Reppoints	SKG-Net	SA-Net
A330	85.0	87.4	66.4	66.4	88.6
A320/321	87.7	73.9	84.9	49.6	86.6
A220	58.7	49.1	49.4	29.8	55.0
ARJ21	55.2	59.0	50.9	37.7	59.7
Boeing737	42.8	39.1	36.6	48.7	41.8
Boeing787	60.5	57.6	41.8	51.6	60.4
other	45.4	46.1	43.1	41.1	47.7
平均值(mAP)	62.2	58.9	53.3	46.4	62.8

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表 7 所提方法中各个模块的影响(%)

Table 7. Influence of each component in the proposed method (%)

方法	P	R	F1	AP_0.5	AP_0.75
Baseline	88.1	81.2	84.5	79.6	60.7
Baseline+SA-Head	88.2	82.1	85.0	80.3	60.8
Baseline+CG-FPN	88.6	81.9	85.1	80.4	60.4
SA-Net	87.5	82.2	84.8	80.4	61.4

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