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Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm

机译：基于改进CFSFDP算法的极化SAR图像无监督分类

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摘要

In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of PolSAR image classes is an important issue. In this article, we propose a novel unsupervised classification method which can accurately estimate the number of classes for PolSAR images. First, the PolSAR image is initialized into many small clusters by using the complementary information from Yamaguchi decomposition and distribution characteristics of data. Second, the improved clustering by fast search and find of density peaks, named as improved CFSFDP algorithm, is introduced to select the appropriate category number. Finally, to improve the representation of each category, the PolSAR data set is classified by an iterative fine-tuning process based on a complex K-Wishart function. The performance of the proposed classification approach is presented and analysed on three real data sets. The experimental results show that the proposed classification method can accurately estimate the category number and enhance the classification accuracy in comparison with other traditional methods. It is also shown that the data distribution characteristic has the additional information beyond the target scattering decomposition, and this information is important for the initialization.
机译：在极化合成孔径雷达（PolSAR）图像处理中，分类数量是PolSAR图像分类的重要因素。因此，如何准确地估计PolSAR图像类别的数量是一个重要的问题。在本文中，我们提出了一种新颖的无监督分类方法，该方法可以准确地估计PolSAR图像的类别数量。首先，利用山口分解的补充信息和数据的分布特性，将PolSAR图像初始化为许多小簇。其次，介绍了通过快速搜索和查找密度峰值而得到的改进聚类，称为改进CFSFDP算法，以选择适当的类别编号。最后，为了改进每个类别的表示，PolSAR数据集通过基于复杂K-Wishart函数的迭代微调过程进行分类。提出的分类方法的性能在三个真实数据集上进行了介绍和分析。实验结果表明，与其他传统方法相比，该分类方法可以准确地估计类别数，提高分类精度。还表明，数据分布特性具有目标散射分解以外的其他信息，并且此信息对于初始化很重要。

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  • 来源
    《International journal of remote sensing》 |2019年第8期|3154-3178|共25页
  • 作者

    Wang Shuang; Hua Wenqiang; Liu Hongying; Jiao Licheng;

  • 作者单位

    Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian, Shaanxi, Peoples R China;

    Xian Univ Posts & Telecommun, Sch Comp Sci & Technol, Shaanxi Key Lab Network Data Anal & Intelligent P, Xian, Shaanxi, Peoples R China;

    Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian, Shaanxi, Peoples R China;

    Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian, Shaanxi, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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