基于GF-7数据的农村地区细粒度建筑物分类
Mingbo Liu1, Ping Wang1, Peng Han1
1National Disaster Reduction Center of China, Ministry of Emergency Management of the People's Republic of China, Beijing 100124, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究引入了一种新的方法,用于使用高分辨率卫星数据进行详细的农村建筑分类. 该方法可以准确地识别建筑类型,并提高高度准确性,帮助灾害管理.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是指地理信息系统.
- 城市和农村规划 城市和农村规划
背景情况:
- 建筑类型信息对于灾害管理,城市化研究和人口建模至关重要.
- 在农村地区使用高分辨率卫星数据进行细粒度建筑物分类仍未得到充分探索.
- 中国的高芬-7 (GF-7) 卫星数据为详细的农村建筑分析提供了潜力.
研究的目的:
- 开发和评估农村地区细粒度建筑物分类的两阶段方法.
- 评估监督分类模型 (XGBoost,随机森林) 对于屋顶类型分类的性能.
- 为了提高斜顶建筑的建筑高度估计的准确性.
主要方法:
- 一种两阶段的方法,结合了监督分类和无监督集群.
- 从GF-7数据中提取建筑物的足迹,高度和多谱信息.
- 实施极端梯度提升 (XGBoost) 和随机森林用于屋顶类型分类.
- 使用几何特征,街景照片和GF-7高度数据,开发基于模板的方法来纠正斜顶建筑高度.
- 基于细粒度建筑类型编码的颜色,形状和高度特征的无监督聚类.
主要成果:
- 该XGBoost模型实现了整体屋顶类型分类准确率为88.89%.
- 建筑高度校正方法将斜顶建筑的根平均平方误差 (RMSE) 从2.28m降至1.20m.
- 生成了细粒度的建筑类型代码,揭示了物理和几何特征以及空间分布.
结论:
- 拟议的两阶段分类方法有效地实现了农村地区的细粒度建筑物分类.
- 该方法增强了对农村建筑特征和空间模式的理解.
- 这种方法在农村灾害管理和规划中具有很大的应用潜力.
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