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基于拖尾分布的高分辨率合成孔径雷达图像建模 被引量:8

Modeling high-resolution synthetic aperture radar images with heavy-tailed distributions
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摘要 基于中心极限定理的合成孔径雷达(SAR)图像统计分布不能反映高分辨率SAR图像尖峰和厚尾的统计特征.文中使用广义中心极限定理,由雷达回波的实部和虚部的对称稳定分布,得到SAR图像的拖尾分布(幅值图像的拖尾Rayleigh分布以及强度图像的拖尾指数分布),并以拖尾Rayleigh分布为例,讨论了拖尾分布的代数拖尾特征以及尖峰厚尾的统计特性.为了实现拖尾分布对高分辨率SAR图像的精确建模,基于第二类统计量,提出了对数累积量的参数估计方法,从而高效估计出拖尾分布的参数.真实SAR图像的建模实例表明,基于广义中心极限定理的拖尾分布可以精确描述高分辨率SAR图像的尖峰和厚尾的统计特征. Statistical distributions of synthetic aperture radar (SAR) images based on central limit theorem cannot reflect the statistical characteristics of sharp peak and heavy tail of high-resolution SAR images. By using the generalized central limit theorem, the heavy-tailed distributions (heavy-tailed Rayleigh distribution for amplitude image and heavy-tailed exponential distribution for intensity image) are obtained from the symmetric stable distributions of real and imaginary parts of echoes. Taking the heavy-tailed Rayleigh distribution as an example, the algebraic tails of heavy-tailed distributions are explained as well as the statistical properties of sharp peak and heavy tail. In order to model the high-resolution SAR images with the heavy-tailed distributions, based on second-kind statistical, Characteristics the log-cumulant estimator is proposed to efficiently estimate the parameters of the heavy-tailed distributions. Modeling experiments on real SAR images demonstrate that the heavy-tailed distributions based on the generalized central limit theorem can accurately describe the sharp-peaked and heavy-tailed statistical characteristics of high-resolution SAR images.
出处 《物理学报》 SCIE EI CAS CSCD 北大核心 2010年第2期998-1008,共11页 Acta Physica Sinica
基金 国家重点基础研究发展计划(批准号:2007CB311006) 国家高技术研究发展计划(批准号:2006AA01Z126) 国家自然科学基金(批准号:60602026) 高等学校博士学科点专项科研基金(批准号:20070698002)资助的课题~~
关键词 高分辨率合成孔径雷达图像 广义中心极限定理 拖尾分布 对数累积量估计 high-resolution synthetic aperture radar images generalized central limit theorem heavy-tailed distributions log-cumulant estimator
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