利用极端梯度提升模型过滤沿海盐沼中的无人机多线光探测与测距数据

IF 4.4 2区 地球科学 Q1 REMOTE SENSING
Drones Pub Date : 2024-01-04 DOI:10.3390/drones8010013
Xixiu Wu, Kai Tan, Shuai Liu, Feng Wang, Pengjie Tao, Yanjun Wang, Xiaolong Cheng
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引用次数: 0

摘要

定量描述沿海盐沼地形及其相应的时空变化,对于制定综合管理计划和阐明碳的动态演变至关重要。多线光探测与测距(LiDAR)具有强大的穿透性能和全新的扫描模式,在盐沼地形测量方面表现出强大的能力。获得高精度地形的前提条件是准确过滤多线激光雷达数据中的盐沼植被点和地面/泥滩点。本研究基于极端梯度提升(即 XGBoost)模型,为无人机多线激光雷达提出了一种新的盐沼植被点云过滤替代方法。根据植被和地面表现出不同几何和辐射特性的基本原理,构建了 XGBoost 模型,以一系列选定的基本几何和辐射度量(即距离、扫描角、高程、法向量和强度)来模拟点类别的关系,其中,根据无人机多线激光雷达的扫描原理和点云空间分布特征,精确估算了每个点不存在的瞬时扫描几何(即距离和扫描角)。根据构建的模型,结合所选特征,可准确、智能地预测每个点的类别。利用无人机 16 线激光雷达系统对所提出的方法在中国上海沿海盐沼进行了测试。结果表明,所提方法的平均 AUC 值和 G 均值分别为 0.9111 和 0.9063。在不同地形和植被生长状况的地区,所提出的方法表现出更强的适用性和通用性,优于传统方法和其他机器学习方法,在点云过滤和分类方面,特别是在地形、土地覆盖和点云分布非常复杂的极端环境中,显示出巨大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model
Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.
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来源期刊
Drones
Drones Engineering-Aerospace Engineering
CiteScore
5.60
自引率
18.80%
发文量
331
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