广义帕累托分布(GPD)在极值统计的POT模型中常常被用来逼近超过阈值u的超出量X_i-u的分布.为解决经典估计方法存在的问题,Zhang(Zhang J,Likelihood moment estimation for the generalized Pareto distribution,Aust N Z J Stat,2007,4...广义帕累托分布(GPD)在极值统计的POT模型中常常被用来逼近超过阈值u的超出量X_i-u的分布.为解决经典估计方法存在的问题,Zhang(Zhang J,Likelihood moment estimation for the generalized Pareto distribution,Aust N Z J Stat,2007,49:69-77)对两参数GPD(GP2)提出一种新的估计方法——似然矩估计(LM),它容易计算且具有较高的渐近有效性.本文将此方法从两参数的情形推广到三参数GPD(GP3),结果表明尺度参数和形状参数估计的渐近性质与以上所提到的文章完全相同.针对GP3的LM估计也具有总是存在、易于计算以及对绝大多数的形状参数具有接近于最小的偏差和均方误差的特点.展开更多
A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
文摘广义帕累托分布(GPD)在极值统计的POT模型中常常被用来逼近超过阈值u的超出量X_i-u的分布.为解决经典估计方法存在的问题,Zhang(Zhang J,Likelihood moment estimation for the generalized Pareto distribution,Aust N Z J Stat,2007,49:69-77)对两参数GPD(GP2)提出一种新的估计方法——似然矩估计(LM),它容易计算且具有较高的渐近有效性.本文将此方法从两参数的情形推广到三参数GPD(GP3),结果表明尺度参数和形状参数估计的渐近性质与以上所提到的文章完全相同.针对GP3的LM估计也具有总是存在、易于计算以及对绝大多数的形状参数具有接近于最小的偏差和均方误差的特点.
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
