SAR图像快速扩散生成方法

doi:10.19665/j.issn1001-2400.20250503

Abstract

Abstract:

Existing deep learning algorithms for Synthetic Aperture Radar (SAR) target classification rely heavily on large datasets.However,the high cost of radar data acquisition and the non-cooperative targets limit data availability,significantly degrading algorithm performance.This limitation restricts the widespread application of radar image classification algorithms.Current methods for generating SAR images using generative adversarial networks face challenges such as a low-quality output,mode collapse,and uncontrollable factors.To address these issues,we propose an accelerated diffusion-based method for SAR image generation to enhance the radar target recognition accuracy.By embedding a cross-attention conditional mechanism,we encode the class and azimuth of targets to guide the generation of SAR images with specified classes and azimuth angles.To overcome the slow generation speed of the diffusion model,we utilize the DPM-Solver to accelerate the sampling process and explore the impact of different hyperparameters on generation quality.Experiments on the MSTAR dataset demonstrate that with 60% of the data,the augmented samples generated by the diffusion model can enhance the classification accuracy of the SAR target classification model from 95.16% to 97.62%.Moreover,the augmented samples generated by the DPM-Solver can further improve the classification accuracy to 97.98%.

Key words: diffusion model, synthetic aperture radar, image generation, target classification

CLC Number:

TP391

DUN Hao, TIAN Chunna, LI Xiangyang, SHAN Xiao, GUO Yujie. Accelerated diffusion-based method for SAR image generation[J].Journal of Xidian University, 2025, 52(3): 217-231.

Figures/Tables 20

References 38

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类别	训练集		测试集
类别	俯仰角/(°)	数量/张	俯仰角/(°)	数量/张
2S1	17	299	15	274
BMP2	17	233	15	195
BRDM2	17	298	15	274
BTR60	17	256	15	195
BTR70	17	233	15	196
D7	17	299	15	274
T62	17	298	15	273
T72	17	232	15	196
ZIL131	17	299	15	274
ZSU23/4	17	299	15	274
总计	2 746		2 425

类别	训练集		测试集
类别	俯仰角/(°)	数量/张	俯仰角/(°)	数量/张
2S1	15,16	116	17	58
BMP2	16	55	17	52
BTR70	16	43	17	49
M1	14,16	78	17	51
M2	14,16	75	17	53
M35	14,16	76	17	53
M548	16	75	17	53
M60	15,16	116	17	60
T72	16	56	17	52
ZSU23	15,16	116	17	58
总计	806		539

步数	二阶			三阶
步数	均匀	二次型	对数信噪比	均匀	二次型	对数信噪比
10	22.13	8.91	5.00	61.49	15.17	14.15
15	21.51	8.29	5.78	67.75	8.32	7.35
20	21.07	7.45	6.06	48.98	6.44	6.55
25	19.23	6.90	6.03	32.49	6.12	6.18
50	11.31	6.22	6.05	10.72	6.03	6.07
100	7.43	6.09	6.00	5.81	6.03	6.03
250	6.21	6.04	6.00	5.99	6.02	6.02

步数	距离得分
7	4.28
8	3.91
9	4.18
10	5.00
11	5.21

数据集	均值	方差	等效视数	辐射分辨率	IS	距离得分	生成时间/(s·张^-1)
MSTAR数据集	28.17	1 134.27	0.758	3.364	1.187
DDPM	31.89	1 637.14	0.673	3.493	1.119	37.65	6.355
DiT-S/2	33.58	1 823.60	0.682	3.494	1.280	8.83	11.622
文中方法	30.56	1 171.69	0.859	3.219	1.177	3.91	0.041
SAMPLE数据集	49.57	838.55	2.99	2.01	1.305
DDPM	48.29	809.60	2.93	2.02	1.338	6.48	6.381
DiT-S/2	52.29	994.41	2.84	2.08	1.684	37.63	11.602
文中方法	50.39	840.20	3.08	1.99	1.344	5.83	0.041

Accelerated diffusion-based method for SAR image generation

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Figures/Tables 20

References 38

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方法	参数量/M	采样步数	浮点运算次数(GFLOPs)
DDPM	113.61	1 000	30 994.20
DiT-S/2	32.47	250	10 889.40
文中方法	5.05	8	42.35

方法	准确率/%	类别	方位角
DDPM^[22]		×	×
CGAN^[12]	95.63	√	×
ACGAN^[13]	96.23	√	×
DiT-S/2^[25]	98.09	√	×
PeaceGAN^[20]	99.42	√	√
文中方法	99.51	√	√

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数据量/%	DiT-S/2生成图像	文中方法生成图像	真实图像
100
70
40
10