SAR image classification based on alpha-stable distribution

SAR image classification based on alpha-stable distribution
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基于α稳定分布的SAR图像分类

DOI:
10.1080/01431161.2010.487878
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发表时间:
2011-03
影响因子:
2.3
通讯作者:
--
中科院分区:
工程技术4区
文献类型:
--
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我们提出了一种分类算法,利用 alpha 稳定分布对合成孔径雷达(SAR)图像的纹理特征进行建模。 SAR 图像首先通过平稳小波变换 (SWT) 进行分解。之后,应用α稳定分布对每个分解尺度下图像的高频子带系数进行建模。然后使用回归型方法来估计α稳定分布参数,从而形成完全描述纹理的特征向量。最后,利用该特征向量基于支持向量机(SVM)方法导出SAR图像分类算法。由于α稳定分布参数的不同组合会导致分类精度的差异,因此还提出了多级支持向量机(MSVM)分类算法来解决该问题。实验结果表明,所提出的SAR图像分类算法是有效的,并且MSVM算法提高了分类性能。此外,我们提出的算法具有较低的计算成本,因为仅处理少量的α稳定分布参数。
We propose a classification algorithm that utilizes the alpha-stable distribution to model the texture features of synthetic aperture radar (SAR) images. The SAR image is first decomposed by stationary wavelet transform (SWT). After that, the alpha-stable distribution is applied to model the high-frequency subband coefficients of the image at each decomposition scale. A regression-type method is then used to estimate the alpha-stable distribution parameters, which form a feature vector that fully describes the texture. Finally, a SAR image classification algorithm is derived by exploiting this feature vector based on the support vector machines (SVM) approach. Because different combinations of alpha-stable distribution parameters contribute to differences in classification precision, a multilevel SVM (MSVM) classification algorithm is also presented to address the issue. Experimental results indicate that the proposed SAR image classification algorithm is effective and the MSVM algorithm improves the classification performance. Moreover, our proposed algorithm has low computational cost as only a small number of the alpha-stable distribution parameters are processed.
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