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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas
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Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas

机译：反向散射系数作为市区全波形机载激光扫描数据分类的一个属性

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Airborne laser scanning (ALS) data are increasingly being used for land cover classification. The amplitudes of echoes from targets, available from full-waveform ALS data, have been found to be useful in the classification of land cover. However, the amplitude of an echo is dependent on various factors such as the range and incidence angle, which makes it difficult to develop a classification method which can be applied to full-waveform ALS data from different sites, scanning geometries and sensors. Additional information available from full-waveform ALS data, such as range and echo width, can be used for radiometric calibration, and to derive backscatter cross section. The backscatter cross section of a target is the physical cross sectional area of an idealised isotropic target, which has the same intensity as the selected target. The backscatter coefficient is the backscatter cross section per unit area. In this study, the amplitude, backscatter cross section and backscatter coefficient of echoes from ALS point cloud data collected from two different sites are analysed based on urban land cover classes. The application of decision tree classifiers developed using data from the first study area on the second demonstrates the advantage of using the backscatter coefficient in classification methods, along with spatial attributes. It is shown that the accuracy of classification of the second study area using the backscatter coefficient (kappa coefficient 0.89) is higher than those using the amplitude (kappa coefficient 0.67) or backscatter cross section (kappa coefficient 0.68). This attribute is especially useful for separating road and grass.rn? 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published byrnElsevier B.V. All rights reserved.
机译：机载激光扫描（ALS）数据正越来越多地用于土地覆盖分类。已发现可从全波形ALS数据获得的目标回波幅度在土地覆盖分类中很有用。然而，回波的幅度取决于各种因素，例如范围和入射角，这使得很难开发一种分类方法，该方法可应用于来自不同站点，扫描几何形状和传感器的全波形ALS数据。可从全波形ALS数据中获得的其他信息（例如范围和回波宽度）可用于辐射校准，并得出反向散射截面。目标的反向散射横截面是理想化各向同性目标的物理横截面，其强度与所选目标相同。反向散射系数是每单位面积的反向散射截面。在这项研究中，根据城市土地覆盖类别，分析了从两个不同地点收集的ALS点云数据回波的振幅，反向散射截面和反向散射系数。决策树分类器在第二个研究区域的数据开发中的应用证明了在分类方法中使用反向散射系数以及空间属性的优势。结果表明，使用后向散射系数（kappa系数0.89）对第二研究区域进行分类的准确性高于使用幅度（kappa系数0.67）或后向散射截面（kappa系数0.68）进行分类的第二个研究区域。此属性对于分隔道路和草地特别有用。 2010国际摄影测量与遥感学会（ISPRS）。由rn Elsevier B.V.保留所有权利。

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