Polarimetric radar target classification using support vector machines
Polarimetric radar target classification using support vector machines
复制标题
使用支持向量机进行偏振雷达目标分类
DOI：
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
F. Sadjadi
中科院分区：
文献类型：
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作者：
F. Sadjadi
本文报道了用于极化雷达目标分类的支持向量机的研究进展。通过对极化发射和接收状态的优化选择，大大提高了对人造目标雷达特征的自动分类能力。然而，最佳偏振态可能是与目标相关的。因此，需要一种最优的分类器来正确标记在不同发射和接收偏振态对时与不同目标相关的雷达特征。为了从垂直和水平两种状态生成不同收发对极化角度下的目标雷达特征，采用了极化合成技术。然后利用每个雷达特征的统计属性进行表征。最后，利用线性函数、多项式、高斯径向基函数、指数基函数、线性样条和基样条函数等多种核函数，开发了支持向量机，并将其应用于实际的全极化雷达数据。结果表明，一个小的偏振角子集足以生成训练分类器所需的签名，以实现偏振多样性签名的最佳分离。此外，在接收者工作特征曲线方面的分类性能表明，高斯核优于我们使用的其他核。
This paper reports on the development of support vector machines for polarimetric radar target classification. Automatic classification of radar signatures of man-made objects has been shown to be improved noticeably by optimum selection of transmitting and receiving states of polarization. However, the optimum polarization states may be target-dependent. Thus the need for an optimum classifier to correctly label radar signatures associated with different targets when sensed at differing pairs of transmitting and receiving polarization states. To generate radar signatures of targets at various transmit-receive pairs of polarization angles from vertical and horizontal states, the technique of polarization synthesis was applied. Then statistical attributes from each radar signature were used for its representation. Finally, support vector machines, using a number of kernels such as linear functions, polynomials, Gaussian radial basis functions, exponential basis functions, linear splines, and basis spline functions, were developed and used on real fully polarimetric radar data. The results indicate that a small subset of polarization angles are sufficient for generating signatures needed for training a classifier for optimal separation of polarimetric-diverse signatures. Moreover, the classification performance in terms of receiver operating characteristic curves shows that Gaussian kernels outperform other kernels that we have used.