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首页> 外文期刊>International journal of remote sensing >Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm
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Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm

机译：基于改进的CFSFDP算法的Polarimetric 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Polariemetric合成孔径雷达（POLSAR）图像处理中，类的数量是POLSAR图像分类的重要因素。因此，如何准确估计Polsar图像类的数量是一个重要问题。在本文中，我们提出了一种新颖的无监督分类方法，可以准确地估计Polsar图像的类数。首先，通过使用来自yamaguchi分解和数据分布特性的互补信息，将POLSAR图像初始化为许多小群集。其次，引入了通过快速搜索和查找密度峰值的改进的聚类，命名为改进的CFSFDP算法，以选择适当的类别号码。最后，为了提高每个类别的表示，POLSAR数据集通过基于复杂的K-Wellart函数的迭代微调过程来分类。提出并分析了所提出的分类方法的性能，并分析了三个真实数据集。实验结果表明，与其他传统方法相比，所提出的分类方法可以准确地估计类别号码并提高分类准确性。还显示，数据分布特性具有超出目标散射分解的附加信息，并且该信息对于初始化是重要的。

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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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