Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption intro...Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.展开更多
南京航空航天大学(Nanjing University of Aeronautics and Astronautics,NUAA)雷达探测与成像团队利用自主研发的无人机载微小型合成孔径雷达(Synthetic aperture radar,SAR)系统针对不同型号的坦克、装甲车和战机等十余类典型军事目...南京航空航天大学(Nanjing University of Aeronautics and Astronautics,NUAA)雷达探测与成像团队利用自主研发的无人机载微小型合成孔径雷达(Synthetic aperture radar,SAR)系统针对不同型号的坦克、装甲车和战机等十余类典型军事目标构建了圆周SAR数据集。通过对多次外场试验数据的高精度成像处理,在多俯仰角单基圆周SAR图像数据集的基础上,扩展了不同双基角组合的双基圆周SAR图像数据集。基于该数据集,本文结合团队在SAR图像目标检测和识别方法及应用方面的研究成果,对基于深度学习的SAR目标检测识别技术进行了回顾和综述,对比了不同神经网络模型在南航无人机载圆周SAR数据集上的检测和识别性能。具体地,在目标检测方面,利用SAR图像固有属性获得目标位置信息并结合单阶段轻量级检测算法,提出利用信息分布规律并结合全局注意力机制捕捉小目标位置信息的检测算法,以提高复杂背景下的小目标检测准确率和效率。在目标识别方面,在通过SAR图像先验信息抑制干扰噪声的基础上,提出利用SAR目标多视角信息联合Transformer的目标识别算法,通过设计视角正则化项以约束多视角之间的关联性从而实现不同视角间的特征融合,提高SAR小目标识别的准确率。从无人机载微型SAR系统对地面目标进行实时检测和识别的实际需求出发,本文还探讨了轻量化检测和识别网络在数字信号处理(Digital signal processing,DSP)平台上的部署方案,同时展示了初步试验结果。最后,本文展望了SAR目标智能检测和识别领域面临的挑战和发展趋势。展开更多
基金National Natural Science Foundation of China(No.42104025)China Postdoctoral Science Foundation(No.2021M702509)+3 种基金Natural Resources Sciences and Technology Project of Hunan Province(No.2022-07)Surveying and Mapping Basic Research Foundation of Key Laboratory of Geospace Environment and Geodesy,Ministry of Education(No.20-01-04)Natural Science Foundation of Hunan Province(No.2024JJ5144)Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway(Changsha University of Science&Technology,No.kfj190805).
文摘Affected by the insufficient information of single baseline observation data,the three-stage method assumes the Ground-to-Volume Ratio(GVR)to be zero so as to invert the vegetation height.However,this assumption introduces much biases into the parameter estimates which greatly limits the accuracy of the vegetation height inversion.Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR.Nevertheless,the similar model parameter values in a multi-baseline model often lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm.To this end,we propose a new step-by-step inversion method applied to the multi-baseline observations.Firstly,an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data,and the regularized estimates of model parameters are obtained by regularization method.Then,the reliable estimates of GVR are determined by the MSE(mean square error)analysis of each regularized parameter estimation.Secondly,the estimated GVR is used to extracts the pure volume coherence,and then the vegetation height parameter is inverted from the pure volume coherence by least squares estimation.The experimental results show that the new method can improve the vegetation height inversion result effectively.The inversion accuracy is improved by 26%with respect to the three-stage method and the conventional solution of multi-baseline.All of these have demonstrated the feasibility and effectiveness of the new method.
文摘南京航空航天大学(Nanjing University of Aeronautics and Astronautics,NUAA)雷达探测与成像团队利用自主研发的无人机载微小型合成孔径雷达(Synthetic aperture radar,SAR)系统针对不同型号的坦克、装甲车和战机等十余类典型军事目标构建了圆周SAR数据集。通过对多次外场试验数据的高精度成像处理,在多俯仰角单基圆周SAR图像数据集的基础上,扩展了不同双基角组合的双基圆周SAR图像数据集。基于该数据集,本文结合团队在SAR图像目标检测和识别方法及应用方面的研究成果,对基于深度学习的SAR目标检测识别技术进行了回顾和综述,对比了不同神经网络模型在南航无人机载圆周SAR数据集上的检测和识别性能。具体地,在目标检测方面,利用SAR图像固有属性获得目标位置信息并结合单阶段轻量级检测算法,提出利用信息分布规律并结合全局注意力机制捕捉小目标位置信息的检测算法,以提高复杂背景下的小目标检测准确率和效率。在目标识别方面,在通过SAR图像先验信息抑制干扰噪声的基础上,提出利用SAR目标多视角信息联合Transformer的目标识别算法,通过设计视角正则化项以约束多视角之间的关联性从而实现不同视角间的特征融合,提高SAR小目标识别的准确率。从无人机载微型SAR系统对地面目标进行实时检测和识别的实际需求出发,本文还探讨了轻量化检测和识别网络在数字信号处理(Digital signal processing,DSP)平台上的部署方案,同时展示了初步试验结果。最后,本文展望了SAR目标智能检测和识别领域面临的挑战和发展趋势。
