信息工程大学学报2025，Vol.26Issue(1)：21-28,8.DOI:10.3969/j.issn.1671-0673.2025.01.004
WGAN-GP数据增强及预训练模型的SAR目标识别方法
SAR Target Recognition Method Based on WGAN-GP Data Enhancement and Pre-Trained Model
摘要
Abstract
Synthetic aperture radar automatic target recognition(SAR ATR)technology is widely uti-lized in military target detection.However,the difficulty in obtaining labeled SAR samples restricts the utilization of existing recognition techniques.A SAR target recognition method,which combines a was-serstein GAN with gradient penalty(WGAN-GP)with a pre-trained model,is proposed.After augmen-tation of small training datasets using WGAN-GP,the data is then fed into a convolutional neural net-work(CNN)model pretrained on the large-scale remote sensing image scene classification(RESISC)dataset for training,ultimately yielding SAR target recognition results.The algorithm's capabilities are evaluated using the moving and stationary target acquisition and recognition(MSTAR)dataset.Experi-mental results indicate WGAN-GP,when utilized,outperforms other generative adversarial networks in SAR sample enhancement.Furthermore,the selection of the RESISC45 dataset is found to effectively enhance the classifier's pretraining ability.Compared to existing research findings,the approach ex-hibits advantages in improving SAR target recognition accuracy and CNN model convergence speed.关键词
合成孔径雷达自动目标识别/带梯度惩罚的生成对抗网络/预训练模型Key words
SAR ATR/WGAN-GP/pre-trained model分类
信息技术与安全科学引用本文复制引用
周明康,张静,朱晨晨..WGAN-GP数据增强及预训练模型的SAR目标识别方法[J].信息工程大学学报,2025,26(1):21-28,8.基金项目
国家自然科学基金(61601516) （61601516）
