基于空中雷达点云的土地覆盖分类,具有可能性方法和多分类器
Danjing Zhao1, Linna Ji1, Fengbao Yang1
1School of Information and Communication Engineering, North University of China, Taiyuan 030051, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
这项研究引入了一种新方法,用于使用空中激光扫描仪点云对城市土地覆盖进行分类. 通过将多个分类器与可能性理论融合,它在具有挑战性的领域显著提高了准确性.
科学领域:
- 地理空间数据分析.
- 遥感是一种远程传感.
- 城市规划是城市规划.
背景情况:
- 空中激光扫描仪 (ALS) 点云为城市土地覆盖研究提供了关键的3D数据.
- 由于决策不确定性,当前的单一分类器方法在模两可的领域中难以准确.
研究的目的:
- 通过减少决策中的不确定性来提高点云分类的准确性.
- 开发一种利用可能性理论的多分类器聚变方法.
主要方法:
- 使用XGBoost来定义特征空间的特征重要性分析.
- 使用两个支持向量机器 (SVM) 作为基本分类器.
- 量化评估分类器输出,以确定混的区域,并计算信心权重.
主要成果:
- 提出的方法成功地识别和解决了模两可的地区的分类不确定性.
- 基于可能性理论的多分类器融合显著提高了分类准确性.
- 在DALES数据集上的验证证实了该方法的有效性.
结论:
- 开发的基于可能性理论的融合方法为改善ALS点云的城市土地覆盖分类提供了强大的解决方案.
- 这种方法提高了数字城市倡议的地理空间数据的可靠性.
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