Unsupervised learning rules for POLSAR images analysis

Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing Pub Date : 2002-11-07 DOI:10.1109/NNSP.2002.1030068

S. Chitroub, A. Houacine, B. Sansal

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引用次数: 10

Abstract

It has been shown (see Chitroub, S. et al., Signal Processing, vol.82, no.1, p.69-92, 2002) that the model for POLSAR (polarimetric synthetic aperture radar) images is a mixture model that results from the product of two distributions, one characterizes the target response and the other characterizes the speckle phenomenon. For scene interpretation purpose, it is desirable to separate between the target response and the speckle information. We propose here to use some unsupervised learning rules for POLSAR images analysis via a PCA-ICA neural network model. Based on its rigorous statistical formulation (see Chitroub et al., Intelligent Data Analysis International Journal, vol.6, no.2, 2002), a neuronal PCA approach for the simultaneous diagonalization of the signal and noise covariance matrices is proposed. The goal is to provide PC images that are uncorrelated and have an improved SNR. Speckle is a non-Gaussian multiplicative noise, and the higher order statistics contain additional information about it. ICA is used to separate the speckle from the PC images and providing new IC images that have an improved contrast. The method has been applied on real POLSAR images. The extracted features are quite effective for scene interpretation.

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POLSAR图像分析的无监督学习规则

它已经被证明(见Chitroub, S. et al.， Signal Processing, vol.82, no. 6)。(1, p.69-92, 2002)， POLSAR(偏振合成孔径雷达)图像模型是两种分布的乘积的混合模型，其中一种分布表征目标响应，另一种表征散斑现象。为了场景解释的目的，最好将目标响应和散斑信息分开。我们在此建议使用一些无监督学习规则，通过PCA-ICA神经网络模型对POLSAR图像进行分析。基于其严格的统计公式(参见Chitroub et al.， Intelligent Data Analysis International Journal, vol.6, no. 6)。(2, 2002)，提出了一种同时对角化信号和噪声协方差矩阵的神经主成分分析方法。目标是提供不相关的PC图像，并具有改进的信噪比。散斑是一种非高斯乘性噪声，高阶统计量包含了关于它的附加信息。ICA用于从PC图像中分离散斑，并提供具有改进对比度的新IC图像。该方法已在真实的POLSAR图像上得到应用。提取的特征对场景解释非常有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing

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