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首页> 外文期刊>GIScience & remote sensing >Curve matching approaches to waveform classification: a case study using ICESat data
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Curve matching approaches to waveform classification: a case study using ICESat data

机译：波形分类的曲线匹配方法：使用ICESat数据的案例研究

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摘要

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback-Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov-Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.
机译：光检测和测距（LiDAR）波形正越来越多地用于许多森林和城市应用中，尤其是用于地物分类。但是，大多数研究都依赖于将波形离散化为多次返回或从波形中提取形状度量。几乎没有探索过直接使用包含最全面，最准确信息的完整波形。我们建议利用完整的波形来测试其使用曲线匹配方法区分具有不同垂直结构的对象的能力。通过扩展最初为基于像素的高光谱图像分类和基于对象的高空间图像分类而设计的方法，开发了两组曲线匹配方法。第一组基于测量未知波形和参考波形之间的曲线相似性，其中包括曲线平方和平方微分面积（CRSSDA），曲线角度映射器（CAM）和Kullback-Leibler（KL）发散。第二组评估其波形的未知和参考累积分布函数（CDF）之间的曲线相似性，包括累积曲线均方根微分面积（CCRSSDA），累积曲线角映射器（CCAM）和Kolmogorov-Smirnov（KS）距离。当使用ICESat波形数据对开放空间，树木和建筑物进行分类时，KL提供了最高的平均分类准确度（87％），紧随其后的是CCRSSDA和CCAM，基于15个随机分组，它们均明显优于KS，CRSSDA和CAM。样本集。

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  • 来源
    《GIScience & remote sensing》 |2016年第6期|739-758|共20页
  • 作者

    Zhou Yuhong; Qiu Fang; Ni Feng; Lou Yifei; Zhang Caiyun; Alfarhan Mohammed; Al-Dosari Ali A.;

  • 作者单位

    Univ Texas Dallas, Dept Geospatial Informat Sci, 800 W Campbell Rd GR31, Richardson, TX 75080 USA;

    Univ Texas Dallas, Dept Geospatial Informat Sci, 800 W Campbell Rd GR31, Richardson, TX 75080 USA;

    Univ Texas Dallas, Dept Geospatial Informat Sci, 800 W Campbell Rd GR31, Richardson, TX 75080 USA;

    Univ Texas Dallas, Dept Math Sci, 800 W Campbell Rd GR31, Richardson, TX 75080 USA;

    Florida Atlantic Univ, Dept Geosci, 777 Glades Rd, Boca Raton, FL 33431 USA;

    King Abdulaziz City Sci & Technol, Natl Ctr Remote Sensing Technol, Riyadh 11442, Saudi Arabia;

    King Saud Univ, Dept Geog, Riyadh 11451, Saudi Arabia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    waveform; classification; curve matching; ICESat; GLAS;

    机译：波形;分类;曲线匹配;ICESat;GLAS;

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