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A new PCA-based method for data compression and enhancement of multi-frequency polarimetric SAR imagery

机译:基于PCA的新数据压缩和多频极化SAR图像增强方法

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摘要

A new PCA-based method for an optimal representation of multi-frequency polarimetric SAR images is proposed. The method performs the simultaneous diagonalization of the signal and multiplicative noise covariance matrices via one Orthogonal matrix. The covariance matrix of the multiplicative noise becomes an identity matrix, which implies that the Variance of the noise in each new image is unity, and is uncorrelated between transformed images. The covariance matrix of the SAR images is transformed to a diagonal matrix whole diagonal elements are ordered in decreasing value, which means that the New images are uncorrelated and will be ordered by their variances (qualities).
机译:提出了一种基于PCA的多频极化SAR图像最优表示方法。该方法通过一个正交矩阵对信号和乘法噪声协方差矩阵同时进行对角化。乘性噪声的协方差矩阵成为一个单位矩阵,这意味着每个新图像中噪声的方差为1,并且在变换后的图像之间不相关。 SAR图像的协方差矩阵被转换为对角矩阵,整个对角元素按递减值排序,这意味着新图像不相关,并将按其方差(质量)排序。

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