基于强散射动态原型引导的小样本SAR飞机细粒度检测识别方法

赵一铭; 吴有明; 戴威; 刁文辉; 孙显

doi:10.12000/JR26060

基于强散射动态原型引导的小样本SAR飞机细粒度检测识别方法

DOI: 10.12000/JR26060 CSTR: 32380.14.JR26060

赵一铭^{1, 2, 3, 4},
吴有明^{1, 2, 3, 4, ,},
戴威^{1, 4, ,},
刁文辉^{1, 2, 3, 4},
孙显^{1, 2, 3, 4}

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.
中国科学院大学电子电气与通信工程学院北京 100190
4.
中国科学院空天信息创新研究院目标认知与应用技术国家级重点实验室北京 100190

基金项目: 国家自然科学基金(62425115)

详细信息

作者简介:
赵一铭，博士生，主要研究方向为遥感图像解译、SAR图像目标检测与识别

吴有明，副研究员，主要研究方向为遥感图像解译、星载SAR图像质量提升

戴　威，副研究员，主要研究方向为计算机视觉、模式识别、遥感图像处理、细粒度视觉分类

刁文辉，副研究员，主要研究方向为计算机视觉、遥感图像分析

孙　显，研究员，主要研究方向为计算机视觉、地理空间数据挖掘、遥感图像理解

通讯作者:
吴有明 wuym01@aircas.ac.cn

戴威 daiwei@aircas.ac.cn

责任主编：杨威 Corresponding Editor: YANG Wei

中图分类号: TN957.51
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出版历程
- 收稿日期: 2026-03-17
- 修回日期: 2026-05-19

A Few-shot SAR fine-grained Aircraft Detection and Recognition Method Guided by Strong Scattering Dynamic Prototypes

ZHAO Yiming^{1, 2, 3, 4},
WU Youming^{1, 2, 3, 4
, ,},
DAI Wei^{1, 4
, ,},
DIAO Wenhui^{1, 2, 3, 4},
SUN Xian^{1, 2, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
4.
National Key Laboratory of Target Cognition and Application Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (62425115)

More Information

Corresponding author: WU Youming, wuym01@aircas.ac.cn; DAI Wei, daiwei@aircas.ac.cn

摘要

摘要: 合成孔径雷达(SAR)图像解译应用场景广泛，SAR飞机检测识别是其中的一个重要分支。由于SAR飞机样本采集与标注难度大，可用训练样本稀缺，亟须发展小样本条件下的SAR飞机检测识别方法。然而，SAR复杂成像环境导致目标特征表达不稳定，检测网络难以自适应应对SAR复杂背景的扰动，限制了小样本条件下飞机检测识别的精度。为此，该文提出了一种基于强散射动态原型引导的小样本SAR飞机细粒度检测识别方法，其关键在于将SAR强散射物理先验融入元度量学习框架，从目标特征增强与网络的自适应适配两个层面协同提升检测识别性能。通过动态原型生成模块提取SAR图像强散射点并构建物理注意力掩码，将高层语义特征锚定在目标的物理几何结构上以提取鲁棒的特征表示，并将其与原型自适应融合生成动态原型；进一步设计动态原型引导模块，基于动态原型实现从语义空间到参数空间的映射，分别对网络权重更新、特征输入和预测输出进行自适应调整，提升模型对新类别的快速适应能力。该文方法有效增强了SAR目标特征表征的稳定性并抑制了背景杂波的干扰，在CSAR-AC数据集上的实验结果表明，该文方法在1-shot和5-shot设置下的检测精度均优于主流小样本目标检测算法，显著提升了复杂场景下的小样本SAR飞机目标检测识别性能。
- 合成孔径雷达 /
- 飞机检测识别 /
- 小样本目标检测 /
- 强散射先验 /
- 元学习
Abstract: Synthetic Aperture Radar (SAR) image interpretation has a wide range of applications, with SAR aircraft detection and recognition being a significant branch. However, collecting and annotating SAR aircraft samples is inherently difficult, leading to a scarcity of training data. Thus, developing few-shot methods for SAR aircraft detection and recognition is urgently needed. The complex SAR imaging environment results in unstable target feature representation, making it difficult for detection networks to adaptively manage disturbances from intricate SAR backgrounds. These factors limit the accuracy of aircraft detection and recognition under few-shot conditions. To address these challenges, this study proposes a few-shot SAR fine-grained aircraft detection and recognition method guided by strong scattering dynamic prototypes. This approach integrates strong scattering physical priors into a meta-metric learning framework. Detection and recognition performance are enhanced through two key aspects: target feature enhancement and task-adaptive network adjustment. A dynamic prototype generation module is introduced to extract strong scattering points from SAR images and create physical attention masks. High-level semantic features are anchored to the physical geometric structure of targets, enabling robust feature representation. These features are then adaptively fused with prototypes to produce dynamic prototypes. A dynamic prototype guidance module, which maps dynamic prototypes from semantic space to parameter space, is also proposed. This enables adaptive adjustments to network weight updates, feature inputs, and prediction outputs, thereby improving the model’s rapid adaptation capability for novel categories. The proposed method enhances the stability of SAR target feature representation and reduces background clutter interference. Experiments conducted on the CSAR-AC dataset demonstrate that the proposed method outperforms mainstream few-shot object detection algorithms under both 1- and 5-shot settings, significantly improving few-shot SAR aircraft detection and recognition performance in complex scenes.
- Synthetic Aperture Radar (SAR) /
- Aircraft detection and recognition /
- Few-shot object detection /
- Strong scattering prior /
- Meta learning

HTML全文

图 1 基于强散射动态原型引导的小样本SAR飞机细粒度目标检测方法整体框架

Figure 1. Overall framework of few-shot SAR fine-grained aircraft detection and recognition method guided by strong scattering dynamic prototypes

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图 2 动态原型生成模块结构

Figure 2. The structure of dynamic prototype generation module

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图 3 不同角点检测算法可视化结果展示

Figure 3. Visualization of different corner detection algorithms

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图 4 动态原型引导模块结构

Figure 4. Structure of dynamic prototype guidance module

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图 5 CSAR-AC中的飞机目标样本标注示例

Figure 5. Sample of aircraft annotated in CSAR-AC

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图 6 CSAR-AC中飞机实例数量统计

Figure 6. Statistics on the number of aircraft instances in the CSAR-AC

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图 7 CSAR-AC中类间特征距离统计

Figure 7. Statistics on the interclass feature distances in the CSAR-AC

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图 8 动态原型生成模块消融实验检测结果可视化

Figure 8. The visualization of the detection results from the dynamic prototype generation module ablation experiment

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图 9 动态原型生成模块消融实验特征图可视化

Figure 9. The visualization of feature maps for dynamic prototype generation module ablation experiments

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图 10 不同均值滤波核大小下的所有类别mAP结果

Figure 10. The mAP results for all categories under different mean filter kernel sizes

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图 11 动态原型引导模块消融实验检测结果可视化

Figure 11. The visualization of the detection results from the dynamic prototype guidance module ablation experiment

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图 12 对比实验检测结果可视化

Figure 12. The visualization of the results from the comparative experiment

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图 13 漏检、虚警与错误识别典型情况可视化

Figure 13. Visualization of typical cases of missed detection, false alarm and incorrect recognition

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表 1 动态原型引导模块网络配置参数汇总

Table 1. Summary of network configuration parameters of the dynamic prototype guidance module

组件	结构	输入维度	输出维度	激活函数
语义条件编码器	Linear→Linear	$ {C}^{\prime} $	$ {D}_{\text{cond}} $	ReLU
缩放系数γ生成	Linear	$ {D}_{\text{cond}} $	$ {C}^{\prime} $	–
偏移系数β生成	Linear	$ {D}_{\text{cond}} $	$ {C}^{\prime} $	–
低秩矩阵生成	Linear	$ {D}_{\text{cond}} $	$ \left({d}_{\text{in}}+{d}_{\text{out}}\right)\cdot r $	–
温度缩放参数T生成	Linear	$ {D}_{\text{cond}} $	1	–
偏移参数s生成	Linear	$ {D}_{\text{cond}} $	1	–

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表 2 CSAR-AC中类内与类间特征距离统计

Table 2. Statistics on the intraclass and interclass feature distances in the CSAR-AC

类别	类内方差	类内平均距离	最近邻类	与最近邻类距离
A220	2608.0251	45.2209	Boeing747	8.8569
A320	2634.7268	47.4453	Other_Aircraft	6.4297
A330	3772.3589	50.6457	Boeing777	7.6809
Airfreighter	2805.2874	45.9796	Fokker-50	9.7756
Boeing737	2687.2690	42.3567	Boeing767	5.2874
Boeing747	4079.0830	52.5342	A220	8.8569
Boeing767	3311.6389	46.1467	Boeing737	5.2874
Boeing777	3853.6699	50.4538	Boeing767	7.0816
Fokker-50	2807.6753	48.5371	Airfreighter	9.7756
Gulfstream	2371.5244	46.7167	Helicopter	15.3461
Helicopter	2981.2732	50.2474	Gulfstream	15.3461
Other_Aircraft	3301.9314	49.7304	A320	6.4297

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表 3 CSAR-AC数据集各类别在不同姿态角度差区间下的同类平均特征距离

Table 3. The average feature distances of the same category in the CSAR-AC under different attitude angle difference intervals for each category

类别	0～30°	30°～60°	60°～90°	90°～120°	120°～150°	150°～180°
A220	61.78	65.07	67.45	68.25	66.50	64.91
A320	66.57	69.24	64.01	64.62	70.72	68.44
A330	72.30	79.87	72.66	79.54	84.00	76.36
Airfreighter	64.19	66.05	66.28	68.25	71.75	65.98
Boeing737	60.42	61.30	62.59	63.25	60.24	64.87
Boeing747	74.64	83.48	78.49	73.70	73.81	79.13
Boeing767	68.07	64.59	66.67	66.20	65.65	71.31
Boeing777	73.79	67.76	75.26	77.19	72.11	79.57
Fokker-50	65.50	70.88	66.63	68.24	70.63	74.03
Gulfstream	64.55	71.55	68.53	66.16	73.22	65.75
Helicopter	67.57	72.01	73.61	73.29	72.09	70.94
Other_Aircraft	70.78	72.89	72.02	72.16	72.09	71.72

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表 4 5-shot设置下新类消融实验mAP结果(%)

Table 4. The mAP results of the novel class ablation experiments under the 5-shot setting (%)

模块		新类						平均值
动态原型生成	动态原型引导	Boeing737	Boeing747	A330	Fokker-50	Helicopter	Gulfstream	平均值
×	×	3.73	7.12	6.76	9.11	6.66	4.18	6.26
√	×	16.95	16.26	18.99	18.67	14.70	16.62	17.03
×	√	12.91	18.82	12.38	7.70	10.81	10.12	12.12
√	√	18.57	30.43	21.23	18.00	17.59	18.61	20.74
注：最优和次优结果分别以加粗和下划线突出表示。

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表 6 角点检测方法消融实验的所有类别mAP结果(%)

Table 6. The mAP results for all categories of corner detection methods ablation experiment (%)

强散射点提取方法	Top-k	Canny	Harris	Harris-Laplace	Shi-Tomasi
1-shot	28.05	28.53	29.67	30.21	30.64
5-shot	34.40	35.28	36.77	37.50	38.35
注：最优结果以加粗突出表示。

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表 7 高斯核参数消融实验的所有类别mAP结果(%)

Table 7. The mAP results for all categories of the Gaussian kernel parameter ablation experiment (%)

高斯核参数	0.5	1.0	2.0	3.0
1-shot	27.41	29.86	30.64	30.45
5-shot	35.97	37.53	38.35	38.01
注：最优结果以加粗突出表示。

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表 8 动态原型引导模块消融实验mAP结果(%)

Table 8. The mAP results of the dynamic prototype guidance module ablation experiment (%)

动态原型引导	新类			基类			全部类别
动态原型引导	1-shot	5-shot	平均值	1-shot	5-shot	平均值	1-shot	5-shot	平均值
×	2.88	6.26	4.57	38.85	48.99	43.92	24.11	25.77	24.94
√	7.45	12.12	9.79	50.53	54.30	52.42	28.94	35.03	31.99
注：最优结果以加粗突出表示。

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表 5 动态原型生成模块消融实验mAP结果(%)

Table 5. The mAP results of the dynamic prototype generation module ablation experiment (%)

动态原型生成	新类			基类			全部类别
动态原型生成	1-shot	5-shot	平均值	1-shot	5-shot	平均值	1-shot	5-shot	平均值
×	2.88	6.26	4.57	38.85	48.99	43.92	24.11	25.77	24.94
√	11.36	17.03	14.20	48.99	58.96	53.98	29.81	36.94	33.38
注：最优结果以加粗突出表示。

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表 9 加入参数低秩调整前后检测头的参数比较

Table 9. Comparison of parameters of detection head with and without parameter low-rank adjustment

参数低秩调整	参数量↓
参数低秩调整	回归头	分类头
×	1280	1536
√	1044 (–18.4%)	1048 (–31.8%)

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表 10 各模块计算效率对比

Table 10. Comparison of computational efficiency of each module

模型	参数量(M)	计算复杂度 FLOPs(G)	推理速度 (FPS)
基线	32.01	165.2	32.7
基线+动态原型生成模块	34.56	180.4	27.5
基线+动态原型引导模块	32.86	168.5	30.2
本文方法	35.41	183.7	25.1

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表 11 对比实验mAP结果

Table 11. The mAP results of comparative experiment

Shot	模型	新类 (%)	基类(%)	全部类别(%)
1-shot	Meta R-CNN^[61]	4.35	45.24	22.79
	Attention-RPN^[62]	6.25	44.78	25.55
	TFA^[40]	5.36	42.64	26.78
	FSCE^[41]	5.39	49.48	26.97
	G-FSDet^[15]	6.15	46.04	27.72
	VFA^[63]	6.80	46.97	25.55
	FOMC^[43]	7.52	47.20	27.35
	本文方法	12.13	49.64	30.64
5-shot	Meta R-CNN^[61]	6.03	48.08	25.77
	Attention-RPN^[62]	9.34	51.71	29.38
	TFA^[40]	11.16	47.73	32.18
	FSCE^[41]	15.91	54.00	33.22
	G-FSDet^[15]	19.66	54.66	35.04
	VFA^[63]	16.36	53.57	36.76
	FOMC^[43]	17.85	52.85	35.05
	本文方法	20.74	55.89	38.35
注：最优结果以加粗突出表示。

下载: 导出CSV

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