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首页> 外文期刊>International journal of remote sensing >Polarimetric SAR image classification by Boosted Multiple-Kernel Extreme Learning Machines with polarimetric and spatial features
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Polarimetric SAR image classification by Boosted Multiple-Kernel Extreme Learning Machines with polarimetric and spatial features

机译：通过具有极化和空间特征的Boosted多核极限学习机对极化SAR图像进行分类

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摘要

In this paper, we propose new approach: Boosted Multiple-Kernel Extreme Learning Machines (BMKELMs), a multiple kernel version of Kernel Extreme Learning Machine (KELM). We apply it to the classification of fully polarized SAR images using multiple polarimetric and spatial features. Compared with other conventional multiple kernel learning methods, BMKELMs exploit KELM with the boosting paradigm coming from ensemble learning (EL) to train multiple kernels. Additionally, different fusion strategies such as majority voting, weighted majority voting, MetaBoost, and ErrorPrune were used for selecting the classification result with the highest overall accuracy. To show the performance of BMKELMs against other state-of-the-art approaches, two L-band fully polarimetric airborne SAR images (Airborne Synthetic Aperture Radar (AIRSAR) data collected by NASA JPL over the Flevoland area of The Netherlands and Electromagnetics Institute Synthetic Aperture Radar (EMISAR) data collected by DLR over Foulum in Denmark) were considered. Experimental results indicate that the proposed technique achieves the highest classification accuracy values when dealing with multiple features, such as a combination of polarimetric coherency and multi-scale spatial features.
机译：在本文中，我们提出了一种新方法：增强型多核极限学习机（BMKELM），多核版本的Kernel Extreme Learning Machine（KELM）。我们将其应用于使用多个极化和空间特征的全极化SAR图像的分类。与其他传统的多核学习方法相比，BMKELM利用来自集成学习（EL）的增强范例来利用KELM来训练多个核。此外，不同的融合策略（例如多数投票，加权多数投票，MetaBoost和ErrorPrune）用于选择具有最高总体准确性的分类结果。为了显示BMKELM相对于其他最新方法的性能，美国国家航空航天局JPL收集了荷兰Flevoland地区的两个L波段全极化机载SAR图像（机载合成孔径雷达（AIRSAR）数据）和电磁研究所合成的考虑了DLR在丹麦的Foulum上收集的孔径雷达（EMISAR）数据。实验结果表明，所提出的技术在处理多个特征（例如极化相干性和多尺度空间特征的组合）时可以获得最高的分类精度值。

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  • 来源
    《International journal of remote sensing》 |2014年第24期|7978-7990|共13页
  • 作者

    Du Peijun; Samat Alim; Gamba Paolo; Xie Xiangjian;

  • 作者单位

    Nanjing Univ, Key Lab Satellite Mapping Technol & Applicat, State Adm Surveying Mapping & Geoinformat China, Nanjing 210008, Jiangsu, Peoples R China|Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210008, Jiangsu, Peoples R China;

    Nanjing Univ, Key Lab Satellite Mapping Technol & Applicat, State Adm Surveying Mapping & Geoinformat China, Nanjing 210008, Jiangsu, Peoples R China|Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210008, Jiangsu, Peoples R China;

    Univ Pavia, Dept Elect Biomed & Comp Engn, I-27100 Pavia, Italy;

    Nanjing Univ, Key Lab Satellite Mapping Technol & Applicat, State Adm Surveying Mapping & Geoinformat China, Nanjing 210008, Jiangsu, Peoples R China|Nanjing Univ, Jiangsu Prov Key Lab Geog Informat Sci & Technol, Nanjing 210008, Jiangsu, 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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