LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine
LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine
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DOI：
10.1109/lgrs.2017.2669304
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发表时间：
2017-03
影响因子：
4.8
通讯作者：
Pedram Ghamisi;B. Höfle
中科院分区：
文献类型：
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作者：
Pedram Ghamisi;B. Höfle
这封信提出了一种新的框架，用于光探测和测距(LiDAR)派生的特征分类。在此背景下，使用聚集的局部点邻域直接从LiDAR点云数据中提取若干特征，包括激光回波比、点高程的方差、平面拟合残差和回波强度。此外，LiDAR数字表面模型(DSM)被输入到我们的分类中。因此，在我们的工作流程中，既考虑了激光雷达栅格DSM，也考虑了聚集到图像中的丰富的几何和后向散射的三维点云信息。这些提取的特征被表征为基本图像，以馈送到消光简档以对空间和上下文信息进行建模。在此基础上，研究了一种适合激光雷达数据的高程信息和空间信息的复合核支持向量机。结果表明，该方法可以在较短的CPU处理时间内，仅使用LiDAR数据就能获得较高的分类精度(例如，在所有15个类别的标准训练和测试样本集上，对基准的Houston LiDAR数据的总体准确率超过86%)。
This letter proposes a novel framework for the classification of light detection and ranging (LiDAR)-derived features. In this context, several features are extracted directly from the LiDAR point cloud data using aggregated local point neighborhoods, including laser echo ratio, variance of point elevation, plane fitting residuals, and echo intensity. Additionally, the LiDAR digital surface model (DSM) is input to our classification. Thus, both the LiDAR raster DSM and also rich geometric and also backscatter 3-D point cloud information aggregated to images are considered in our workflow. These extracted features are characterized as base images to be fed to extinction profiles to model spatial and contextual information. Then, a composite kernel support vector machine is investigated to efficiently integrate the elevation and spatial information suitable for the LiDAR data. Results indicate that the proposed method can obtain high classification accuracy using LiDAR data alone (e.g., more than 86% overall accuracy on the benchmark Houston LiDAR data using the standard set of training and test samples on all 15 classes) in a short CPU processing time.