首页|基于脉冲神经网络的轻量化SAR图像舰船识别算法

基于脉冲神经网络的轻量化SAR图像舰船识别算法

Lightweight Ship Recognition Algorithm Based on SNN in SAR Images

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针对传统方法进行合成孔径雷达(SAR)图像目标识别存在参数多、能耗高等问题,提出了一种基于脉冲神经网络(SNN)的轻量化SAR图像舰船识别算法.首先,利用视觉注意力机制提取SAR图像视觉显著图,采用泊松编码器进行脉冲序列编码,能抑制背景噪声干扰.然后,结合泄漏整合发射(LIF)脉冲神经元和卷积神经网络,构建融合时序信息的SNN模型,能实现SAR图像舰船识别.最后,采用反正切函数作为反向传播时脉冲发射函数的梯度替代函数对SNN模型进行优化,能解决模型难以训练的问题.实验结果表明所提算法具有高精度、少参数、高效率和低能耗等优势,能实现SAR图像高效准确舰船识别.
Due to the more parameters and higher energy-consumption in the traditional methods for the synthetic aperture radar(SAR)image target recognition,this paper proposes a lightweight ship recognition algorithm based on the spiking neural network(SNN)in SAR images.Firsty,the visual attention mechanism is adopted to extract the visual saliency map from SAR images,and the Poisson encoder is adopted for the spike train encode,which can suppress the background noise interference.Then,combined with the leaky integrate-and-fire(LIF)spiking neuron and convolutional neural network,the SNN model integrating the time series information is constructed,which can realize the ship recognition in SAR images.Finally,the SNN model is optimized by using the arctangent function as the surrogate gradient function of the spiking emission function during the backpropagation,which can solve the problem that the SNN model is difficult to train.The experiment results show that the proposed algorithm has higher accuracy,fewer parameters,higher efficiency,and lower energy-consumption,which can achieve efficient and accurate ship recognition in SAR images.

synthetic aperture radar(SAR)imageship recognitionspiking neural network(SNN)lightweight

谢洪途、陈佳兴、张琳、朱楠楠

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中山大学·深圳 电子与通信工程学院,广东 深圳 518107

空军预警学院,湖北 武汉 430019

中山大学 系统科学与工程学院,广东 广州 510275

合成孔径雷达图像 舰船识别 脉冲神经网络 轻量化

广东省基础与应用基础研究基金广东省基础与应用基础研究基金深圳市科技计划深圳市科技计划国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金

2023A15150115882021A1515010768202206193000001202208151717230026220346562001523622016146210593

2024

10.12068/j.issn.1005-3026.2024.04.003

东北大学学报(自然科学版)
东北大学

东北大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.507
ISSN：1005-3026
年,卷(期)：2024.45(4)
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