A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR
A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR
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DOI：
10.3390/rs70911344
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发表时间：
2015-09
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通讯作者：
Chuanfa Chen;Yanyan Li;Changqing Yan;Honglei Dai;Guolin Liu
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作者：
Chuanfa Chen;Yanyan Li;Changqing Yan;Honglei Dai;Guolin Liu
光探测和测距（LiDAR）技术是目前在数字高程模型（DEM）构建中收集高密度高程点的最重要工具之一。然而，高密度数据总是导致数据处理中严重的时间和内存消耗问题。在本文中，我们开发了一种基于薄板样条（TPS）的特征保留（TPS-F）方法，通过选择一定数量的重要地形点并从原始数据集中提取地貌特征来减少激光雷达衍生的地面数据，以保持构造的DEM尽可能高的精度，同时最大限度地保留地形特征。我们采用了四个具有不同地形（即平坦、起伏、丘陵和山区）的研究地点来分析 DEM 构建背景下 TPS-F 用于 LiDAR 数据缩减的性能。这些结果与基于 TPS 的无特征算法 (TPS-W) 和两种经典数据选择方法（包括最大 z 容差 (Max-Z) 和随机方法）的结果进行了比较。结果表明，无论地形特征如何，基于 TPS 的方法的两个版本（即 TPS-F 和 TPS-W）在误差范围和均方根误差方面始终比经典方法更准确。此外，在流线匹配率（SMR）方面，TPS-F具有更好的地貌特征保留能力，尤其是对于山地地形。例如，TPS-F在山区的平均SMR为89.2%，而TPS-W、max-Z和随机方法的平均SMR分别为56.6%、34.7%和35.3%。
Light detection and ranging (LiDAR) technique is currently one of the most important tools for collecting elevation points with a high density in the context of digital elevation model (DEM) construction. However, the high density data always leads to serious time and memory consumption problems in data processing. In this paper, we have developed a thin plate spline (TPS)-based feature-preserving (TPS-F) method for LiDAR-derived ground data reduction by selecting a certain amount of significant terrain points and by extracting geomorphological features from the raw dataset to maintain the accuracy of constructed DEMs as high as possible, while maximally keeping terrain features. We employed four study sites with different topographies (i.e., flat, undulating, hilly and mountainous terrains) to analyze the performance of TPS-F for LiDAR data reduction in the context of DEM construction. These results were compared with those of the TPS-based algorithm without features (TPS-W) and two classical data selection methods including maximum z-tolerance (Max-Z) and the random method. Results show that irrespective of terrain characteristic, the two versions of TPS-based approaches (i.e., TPS-F and TPS-W) are always more accurate than the classical methods in terms of error range and root means square error. Moreover, in terms of streamline matching rate (SMR), TPS-F has a better ability of preserving geomorphological features, especially for the mountainous terrain. For example, the average SMR of TPS-F is 89.2% in the mountainous area, while those of TPS-W, max-Z and the random method are 56.6%, 34.7% and 35.3%, respectively.