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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Unsupervised classification algorithm based on EM method for polarimetric SAR images
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Unsupervised classification algorithm based on EM method for polarimetric SAR images

机译：基于EM方法的极化SAR图像无监督分类算法

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摘要

In this work we develop an iterative classification algorithm using complex Gaussian mixture models for the polarimetric complex SAR data. It is a non supervised algorithm which does not require training data or an initial set of classes. Additionally, it determines the model order from data, which allows representing data structure with minimum complexity. The algorithm consists of four steps: initialization, model selection, refinement and smoothing. After a simple initialization stage, the EM algorithm is iteratively applied in the model selection step to compute the model order and an initial classification for the refinement step. The refinement step uses Classification EM (CEM) to reach the final classification and the smoothing stage improves the results by means of non-linear filtering. The algorithm is applied to both simulated and real Single Look Complex data of the EMISAR mission and compared with the Wishart classification method. We use confusion matrix and kappa statistic to make the comparison for simulated data whose ground-truth is known. We apply Davies-Bouldin index to compare both classifications for real data. The results obtained for both types of data validate our algorithm and show that its performance is comparable to Wishart's in terms of classification quality. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译：在这项工作中，我们针对极化复合SAR数据开发了一种使用复杂高斯混合模型的迭代分类算法。这是一种无监督算法，不需要训练数据或初始类集。此外，它可以根据数据确定模型顺序，从而可以以最小的复杂度表示数据结构。该算法包括四个步骤：初始化，模型选择，细化和平滑。在简单的初始化阶段之后，将EM算法迭代应用到模型选择步骤中，以计算模型顺序和优化步骤的初始分类。细化步骤使用分类EM（CEM）达到最终分类，并且平滑阶段通过非线性滤波改善了结果。该算法适用于EMISAR任务的模拟和实际单眼复杂数据，并与Wishart分类方法进行了比较。我们使用混淆矩阵和kappa统计量对已知真实性的模拟数据进行比较。我们使用Davies-Bouldin指数比较两种分类的真实数据。两种数据类型的结果都验证了我们的算法，并表明该算法的性能在分类质量上可与Wishart媲美。 （C）2016国际摄影测量与遥感学会（ISPRS）。由Elsevier B.V.发布。保留所有权利。

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    Fernandez-Michelli J. I.; Hurtado M.; Areta J. A.; Muravchik C. H.;

  • 作者单位

    Univ Nacl La Plata, Fac Ingn, Dept Elect, LEICI, Calle 1 & 47,B1900TAG, RA-1900 La Plata, Buenos Aires, Argentina|Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina;

    Univ Nacl La Plata, Fac Ingn, Dept Elect, LEICI, Calle 1 & 47,B1900TAG, RA-1900 La Plata, Buenos Aires, Argentina|Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina;

    Univ Nacl Rio Negro, San Carlos De Bariloche, Argentina|Consejo Nacl Invest Cient & Tecn, RA-1033 Buenos Aires, DF, Argentina;

    Univ Nacl La Plata, Fac Ingn, Dept Elect, LEICI, Calle 1 & 47,B1900TAG, RA-1900 La Plata, Buenos Aires, Argentina|CIC PBA, La Plata, Buenos Aires, Argentina;

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  • 正文语种 eng
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  • 关键词

    SAR images; Classification; Expectation maximization; Mixture reduction; Gaussian mixture; BIC;

    机译：SAR图像;分类;期望最大化;混合减少;高斯混合;BIC;

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