南京航空航天大学学报（英文版）2025，Vol.42Issue(4)：525-540,16.DOI:10.16356/j.1005-1120.2025.04.008
基于卷积神经网络的稀疏SAR目标梯度加权类激活映射分类方法
A CNN-Based Method for Sparse SAR Target Classification with Grad-CAM Interpretation
摘要
Abstract
In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the"black box"effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN's differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.关键词
稀疏合成孔径雷达/卷积神经网络/集成学习/目标分类/SAR解译Key words
sparse synthetic aperture radar/convolutional neural network(CNN)/ensemble learning/target classification/SAR interpretation分类
信息技术与安全科学引用本文复制引用
姬忠远,张晶晶,刘泽昊,李国旭..基于卷积神经网络的稀疏SAR目标梯度加权类激活映射分类方法[J].南京航空航天大学学报（英文版）,2025,42(4):525-540,16.基金项目
This work was supported in part by the National Natural Science Foundation(Nos.62271248,62401256),in part by the Natural Science Foundation of Ji-angsu Province(Nos.BK20230090,BK20241384),and in part by the Key Laboratory of Land Satellite Remote Sens-ing Application,Ministry of Natural Resources of China(No.KLSMNR-K202303). （Nos.62271248,62401256）
