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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Spatially Nonstationary Anisotropic Texture Analysis in SAR Images
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Spatially Nonstationary Anisotropic Texture Analysis in SAR Images

机译：SAR图像的空间非平稳各向异性纹理分析。

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摘要

This paper deals with spatial analysis of texture in synthetic aperture radar (SAR) images. A new parametric model for local two-point statistics of the image is introduced, in order to characterize the spatially nonstationary and anisotropic behavior of the image. The texture is first modeled by a nonstationary Gaussian process resulting from the convolution of a Gaussian white noise with a field of anisotropic Gaussian kernel with spatially varying parameters. Hence, under the hypothesis of locally stationary signal, the analytic expression of the local autocovariance is derived. It is then explained how to simulate nonstationary K-distributed random fields by combining the new model with an already existing simulation method. A method for parameter estimation is then introduced. This method, based on the statistical product model, first corrects the speckle contribution to the local autocovariance and estimates the parameters of the model by analyzing the shape of the autocovariance. The algorithm is then evaluated over simulated and experimental data. Stationary simulations permit to show that, for a sufficient sample size, the estimator is unbiased. A test over a nonstationary simulation proves the ability of the algorithm to capture the spatial fluctuations of the texture. Finally, the method is applied to the experimental SAR data, and it is shown that a large amount of spatial information may be retrieved from the data.
机译：本文处理合成孔径雷达（SAR）图像中纹理的空间分析。为了表征图像的空间非平稳和各向异性行为，引入了用于图像的局部两点统计的新参数模型。首先通过非平稳的高斯过程对纹理进行建模，该过程是由高斯白噪声与各向异性高斯核场（具有空间变化参数）的卷积而产生的。因此，在局部平稳信号的假设下，推导了局部自协方差的解析表达式。然后说明了如何通过将新模型与已经存在的仿真方法相结合来仿真非平稳K分布随机场。然后介绍一种用于参数估计的方法。该方法基于统计乘积模型，首先校正对局部自协方差的散斑影响，然后通过分析自协方差的形状来估计模型的参数。然后根据仿真和实验数据评估该算法。平稳仿真表明，对于足够大的样本量，估计量是无偏的。对非平稳模拟的测试证明了该算法捕获纹理空间波动的能力。最后，将该方法应用于实验性SAR数据，结果表明可以从该数据中检索到大量的空间信息。

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