融合特征金字塔和自注意力机制的SAR三维点云目标识别方法  

Fusing Feature Pyramid and Self-Attention Mechanism for SAR 3D Point Cloud Target Recognition

作　　者：管浩良 张广滨 王岩[1,2,3] GUAN Haoliang;ZHANG Guangbin;WANG Yan(Radar Technology Research Institute,Beijing Institute of Technology,Beijing 100081,China;Yangtze Delta Region Academy of Beijing Institute of Technology,Jiaxing,Zhejiang 314019,China;Beijing Institute of Technology Chongqing Innovation Center,Chongqing 401120,China)

机构地区：[1]北京理工大学雷达技术研究院,北京100081 [2]北京理工大学长三角研究院,浙江嘉兴314019 [3]北京理工大学重庆创新中心,重庆401120

出　　处：《信号处理》2025年第1期70-83,共14页Journal of Signal Processing

基　　金：国家自然科学基金(62331007,62271053);北京市科技新星项目(20220484109,20230484452)。

摘　　要：合成孔径雷达(Synthetic Aperture Radar, SAR)能够获取目标散射特征图像,是目标识别的重要途径,但传统SAR二维图像存在高度维叠掩问题,严重影响目标识别精度。SAR三维成像通过多次观测在高度维形成合成孔径、提高分辨率,能够区分叠掩目标,是SAR领域的前沿方向。由于SAR三维图像采用间隔不定、无序排列的点云数据格式,而基于卷积神经网络架构的SAR二维方法聚焦等间隔、固定排列的像素数据格式,难以直接扩展至SAR三维点云识别。现有SAR三维识别方法将光学识别网络直接迁移,但由于SAR三维图像具有点云稀疏、散射强度分布不均匀等特征,而光学识别网络多面向稠密点云,且通常难以充分利用散射强弱信息,导致在SAR三维识别应用中性能下降。为此,本文提出一种融合特征金字塔和自注意力机制的SAR三维点云目标识别神经网络。该方法利用特征金字塔构建并融合多层级特征,同时提升对点云浅层特征和深层特征的利用能力,解决SAR三维点云稀疏导致的信息损失问题;利用自注意力机制自适应调整目标点云局部语义联系,增强网络对强散射区域特征提取能力,降低弱散射区域特征的影响,解决SAR三维点云散射强度分布不均匀导致网络目标识别精度下降问题。搭建微波暗室缩比成像系统,采集8类地面车辆目标三维数据制作数据集,开展目标识别性能对比实验、消融实验与特征低维可视化实验,实验结果验证了所提方法在识别精度上的优势。Synthetic aperture radar(SAR)is increasingly recognized as a critical method for target recognition that can obtain the scattering feature image of the target.However,the two-dimensional(2D)image of traditional SAR has a problem of height direction occlusion effect,which seriously affects the accuracy of target recognition.Through multiple observations,SAR 3D imaging can form a synthetic aperture in the height dimension,improve the resolution,and distinguish overlapping targets,which is the future direction of SAR research.However,compared with SAR 2D images,SAR 3D images have critical features such as sparse point clouds and uneven distribution.The existing SAR 2D recognition methods find it difficult to effectively handle these features and have poor recognition accuracy.Existing SAR 3D recognition methods directly transfer the optical recognition network.However,owing to the characteristics of SAR 3D images,such as sparse point clouds and uneven scattering intensity distribution,the performance of SAR 3D recognition applications is degraded because the optical recognition network is mostly oriented to dense point clouds,and it is usually difficult to make full use of the scattering information.To address these problems,this paper introduces a SAR 3D point cloud target recognition neural network fusing feature pyramid and self-attention mechanism,which significantly enhances the accuracy of SAR 3D target recognition.Specifically,the proposed method uses the feature pyramid to construct and fuse multi-level features,which improves the utilization ability of shallow features and deep features of the point cloud,and solves the problem of information loss caused by the sparsity of the SAR 3D point cloud.The self-attention mechanism is employed to adaptively adjust the local semantic relationship of the target point cloud,enhance the network ability to extract the features of strong scattering areas,reduce the influence of the features of weak scattering areas,and solve the problem of target recognition accuracy deg

关 键 词：合成孔径雷达 三维识别 点云稀疏 散射强度分布不均匀 特征金字塔 自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]