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3DSARBuSim 1.0:人造建筑高分辨星载SAR三维成像仿真数据集

3DSARBuSim 1.0: High-Resolution Space Borne SAR 3D Imaging Simulation Dataset of Man-Made Buildings
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摘要 层析合成孔径雷达(Tomographic Synthetic Aperture Radar, TomoSAR)成像技术可有效解决陡峭地形叠掩恢复难题,因此成为城市测绘技术的研究热点之一。基于公开数据集的评估是TomoSAR算法研究与系统论证的必要过程,但目前存在的公开数据集缺乏相应的地物模型真值,无法对算法进行定量验证。为解决这一问题,并进一步推动TomoSAR技术的发展,该文首先提出一种基于射线追踪的先进星载雷达模拟器(Ray Tracing Space Borne Radar Advanced Simulator, RT-SBRAS),相较过往方法,该模拟器可快速稳定地模拟复杂建筑物星载SAR图像。基于此,构建了人造建筑物高分辨SAR三维成像仿真(3D SAR Building Simulation, 3DSARBuSim)数据集的1.0版本,其中包含8个典型建筑物场景的双频段多航过全链路仿真数据。最后给出正交匹配追踪(Orthogonal Matching Pursuit, OMP)算法和双频OMP算法在所提数据集上的验证实验,该数据集可对算法进行清晰、准确的定量比较。 Tomographic Synthetic Aperture Radar(TomoSAR)can effectively recover the information of ground objects in steep terrain,and is one of the research hotspots in urban mapping.However,the current public data sets lack the true values of the object models,and cannot quantitatively verify the TomoSAR algorithm.To solve this problem and further promote the development of TomoSAR technology,this paper first proposes an RT-SBRAS(Ray Tracing Based Space Borne Radar Advanced Simulator),which can quickly and stably simulate the spaceborne SAR images of complex buildings compared with previous methods.Based on this,the 1.0 version of the 3D SAR Building Simulation(3DSARBuSim)data set is constructed,which contains the full-link simulation data of eight typical building scenes in dual-band and multi-pass.Finally,Orthogonal Matching Pursuit(OMP)and dual-frequency OMP algorithms are verified on the proposed data set,and the data set can provide clear and accurate quantitative comparison for the algorithms.
作者 焦润之 邓嘉 韩亚权 黄海风 王青松 赖涛 王小青 JIAO Runzhi;DENG Jia;HAN Yaquan;HUANG Haifeng;WANG Qingsong;LAI Tao;WANG Xiaoqing(School of Electronics and Communication Engineering,Sun Yat-sen University,Shenzhen 518107,China)
出处 《电子与信息学报》 EI CAS CSCD 北大核心 2024年第7期2681-2693,共13页 Journal of Electronics & Information Technology
基金 国家自然科学基金(62071499,62273365)。
关键词 合成孔径雷达(SAR) 层析成像 双频 数据集 Synthetic Aperture Radar(SAR) Tomography Dual-frequency Data set
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