Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data
Enhanced Dictionary-Based SAR Amplitude Distribution Estimation and Its Validation With Very High-Resolution Data
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DOI：
10.1109/lgrs.2010.2053517
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发表时间：
2011
影响因子：
4.8
通讯作者：
V. Krylov;G. Moser;S. Serpico;J. Zerubia
中科院分区：
文献类型：
--
作者：
V. Krylov;G. Moser;S. Serpico;J. Zerubia
本文研究了单通道合成孔径雷达(SAR)图像的幅度概率密度函数(Pdf)估计问题。为了解决这一问题，提出了一种新的灵活的方法，将最近提出的基于字典的随机期望最大化方法(为中分辨率SAR开发)扩展到极高分辨率(VHR)卫星图像，并通过引入一种新的混合分量数量估计方法来增强该方法，该方法允许显著降低其计算复杂性。本文的研究重点是非均匀统计量的估计，并以上一代卫星SAR系统TerraSAR-X和Cosmo-SkyMed获取的VHR SAR图像为例进行了验证。这幅VHR图像允许欣赏各种地面材料，导致高度混合的分布，因此提出了一个迄今尚未解决的困难估计问题。我们还对最新的SAR特定pdf模型的扩展词典进行了实验研究，并考虑了词典的精化问题。
In this letter, we address the problem of estimating the amplitude probability density function (pdf) of single-channel synthetic aperture radar (SAR) images. A novel flexible method is developed to solve this problem, extending the recently proposed dictionary-based stochastic expectation maximization approach (developed for a medium-resolution SAR) to very high-resolution (VHR) satellite imagery, and enhanced by introduction of a novel procedure for estimating the number of mixture components, that permits to reduce appreciably its computational complexity. The specific interest is the estimation of heterogeneous statistics, and the developed method is validated in the case of the VHR SAR imagery, acquired by the last-generation satellite SAR systems, TerraSAR-X and COSMO-SkyMed. This VHR imagery allows the appreciation of various ground materials resulting in highly mixed distributions, thus posing a difficult estimation problem that has not been addressed so far. We also conduct an experimental study of the extended dictionary of state-of-the-art SAR-specific pdf models and consider the dictionary refinements.