现代雷达2025，Vol.47Issue(12)：14-21,8.DOI:10.16592/j.cnki.1004-7859.2025026
两步域自适应少样本SAR图像车辆目标识别
Target Recognition of Few-sample SAR Images Based on Two-step Domain Adaptation
摘要
Abstract
Convolutional neural networks(CNNs)have achieved widespread applications in image recognition domain due to their superior performances.However,in the context of synthetic aperture radar(SAR)image-based ground vehicle target recognition,CNN models are prone to overfitting during the training phases due to the lacks of large-scale real-measured datasets.While SAR images based on target scattering characteristics,obtained through electromagnetic simulation,are relatively easier to acquire,there exists a significant domain discrepancies between simulated and real-measured images,therefore,directly using simulated im-ages for training leads to domain shifts,which severely impacts recognition performances.To address this issue,an algorithm that utilizes electromagnetic simulation SAR images for auxiliary training is proposed in this paper.The approach employs style transfer to reduce the visual differences between simulated and real images and achieve feature alignment through adversarial learning.Do-main adaptation is implemented progressively at both the image pixel and feature levels,enabling the feature extraction network to capture common target classification features from both domains.Experimental results on a typical SAR vehicle target dataset dem-onstrate that when the ratio of real-measured to simulated training data reaches 3∶10,the recognition accuracy of the proposed method achieves approximately 95％,outperforming classical domain adaptation algorithms and significantly enhancing the general-ization capabilities of SAR target recognition models.关键词
合成孔径雷达车辆目标/卷积神经网络/特征对齐/风格迁移/域自适应Key words
vehicle targets in synthetic aperture radar(SAR)/convolutional neural network(CNN)/feature alignment/style transfer/domain adaptation分类
信息技术与安全科学引用本文复制引用
周保一,陈诗琪,杨勇..两步域自适应少样本SAR图像车辆目标识别[J].现代雷达,2025,47(12):14-21,8.基金项目
国家自然科学基金资助项目(62401581,62401574) （62401581,62401574）
