...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine
【24h】

LiDAR Data Classification Using Extinction Profiles and a Composite Kernel Support Vector Machine

机译：使用消光轮廓和复合核支持向量机的LiDAR数据分类

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

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.
机译：这封信为光检测和测距（LiDAR）衍生的特征的分类提出了一种新颖的框架。在这种情况下，使用聚合的局部点邻域直接从LiDAR点云数据中提取了几个特征，包括激光回波比，点高程的变化，平面拟合残差和回波强度。此外，将LiDAR数字表面模型（DSM）输入到我们的分类中。因此，在我们的工作流程中，既考虑了LiDAR栅格DSM，也考虑了聚合到图像的丰富几何图形以及反向散射3D点云信息。这些提取的特征被表征为基础图像，该基础图像将被馈送到灭绝轮廓以对空间和背景信息进行建模。然后，研究了一种复合内核支持向量机，以有效地整合适合LiDAR数据的海拔和空间信息。结果表明，所提出的方法仅使用LiDAR数据即可获得较高的分类精度（例如，使用所有15个类别的标准训练和测试样本集，在基准的Houston LiDAR数据上，整体准确性超过86％） 。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号