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面向SAR图像目标分类的CNN模型可视化方法

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卷积神经网络(CNN)在合成孔径雷达(SAR)图像目标分类任务中应用广泛.由于网络工作机理不透明,CNN模型难以满足高可靠性实际应用的要求.类激活映射方法常用于可视化CNN模型的决策区域,但现有方法主要基于通道级或空间级类激活权重,且在SAR图像数据集上的应用仍处于起步阶段.基于此,该文从神经元特征提取能力和网络决策依据两个层面出发,提出了一种面向SAR图像的CNN模型可视化方法.首先,基于神经元的激活值,对神经元在其感受野范围内的目标结构学习能力进行可视化,然后提出一种通道-空间混合的类激活映射方法,通过对SAR图像中的重要区域进行定位,为模型的决策过程提供依据.实验结果表明,该方法给出了模型在不同设置下的可解释性分析,有效拓展了卷积神经网络在SAR图像上的可视化应用.
CNN Model Visualization Method for SAR Image Target Classification
Convolutional Neural Network(CNN)is widely used for image target classifications in Synthetic Aperture Radar(SAR),but the lack of mechanism transparency prevents it from meeting the practical application requirements,such as high reliability and trustworthiness.The Class Activation Mapping(CAM)method is often used to visualize the decision region of the CNN model.However,existing methods are primarily based on either channel-level or space-level class activation weights,and their research progress is still in its infancy regarding more complex SAR image datasets.Based on this,this paper proposes a CNN model visualization method for SAR images,considering the feature extraction ability of neurons and their current network decisions.Initially,neuronal activation values are used to visualize the capability of neurons to learn a target structure in its corresponding receptive field.Further,a novel CAM-based method combined with channel-wise and spatial-wise weights is proposed,which can provide the foundation for the decision-making process of the trained CNN models by detecting the crucial areas in SAR images.Experimental results showed that this method provides interpretability analysis of the model under different settings and effectively expands the application of CNNs for SAR image visualization.

Synthetic Aperture Radar(SAR)VisualizationConvolutional Neural Network(CNN)Class Activation Mapping(CAM)Neurons

李妙歌、陈渤、王东升、刘宏伟

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西安电子科技大学雷达信号处理全国重点实验室 西安 710071

合成孔径雷达 可视化分析 卷积神经网络 类激活映射 神经元

国家自然科学基金陕西省青年创新团队项目中央高校基本科研业务费专项中央高校基本科研业务费专项高等学校学科创新引智计划(111计划)

U21B2006QTZX23037QTZX22160B18039

2024

10.12000/JR23107

雷达学报
中国科学院电子学研究所 中国雷达行业协会

雷达学报

CSTPCD北大核心EI
影响因子：0.667
ISSN：2095-283X
年,卷(期)：2024.13(2)
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