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PolSAR image classification using a semi-supervised classifier based on hypergraph learning

机译：基于超图学习的半监督分类器进行PolSAR图像分类

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摘要

This letter presents a novel semi-supervised method based on hypergraph learning for polarimetric synthetic aperture radar (PolSAR) image classification. Compared with the classic support vector machine, simple-graph learning, k-nearest neighbour (k-NN) and semi-supervised discriminant analysis (SDA) classifiers, the proposed method achieves better performance with fewer labelled points for PolSAR imagery. A hyper-spectral image is used for comparison with use of PolSAR imagery, and the proposed method is found to be inferior to k-NN and SDA for the hyperspectral image. The performance of our method is evaluated in single, dual and full-polarization cases, respectively. The results demonstrate that the performance of our method in the full-polarization case is superior to that in either single or dual-polarization case.
机译：这封信提出了一种基于超图学习的新型半监督方法，用于极化合成孔径雷达（PolSAR）图像分类。与经典支持向量机，简单图学习，k最近邻（k-NN）和半监督判别分析（SDA）分类器相比，该方法以更少的标记点实现了PolSAR图像的更好性能。使用高光谱图像与PolSAR图像进行比较，发现该方法比高光谱图像的k-NN和SDA逊色。我们的方法的性能分别在单极化，双极化和全极化情况下进行评估。结果表明，我们的方法在全极化情况下的性能优于单极化或双极化情况。

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  • 来源
    《Remote sensing letters》 |2014年第6期|386-395|共10页
  • 作者

    Binghui Wei; Jun Yu; Cheng Wang; Hongyi Wu; Jonathan Li;

  • 作者单位

    Department of Computer Science, Xiamen University, Xiamen, China;

    School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China;

    Department of Computer Science, Xiamen University, Xiamen, China;

    Department of Computer Science, Xiamen University, Xiamen, China;

    Department of Computer Science, Xiamen University, Xiamen, China, Faculty of Environment, University of Waterloo, Waterloo, Canada;

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