A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR

Abstract

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 OPEN ACCESS Remote Sens. 2015, 7 11345 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.

Local Influence for the Thin-Plate Spline Generalized Linear Model

“…Furthermore, the TPS-GLM allow modeling non-linear joint interaction effects due to some covariates, as well as the effects of coordinates in spatial data, making them a useful tool to model dynamic pattern in different scientific areas, such as environment, agronomy, ecology, and so on. Some of the main works related to thin-plate spline technique are Duchon [2,3], Bookstein [4], and Chen et al [5], while in the context of statistical modeling, Wahba [6], Green and Silverman [7], Wood [8], and Moraga et al [9], can be mentioned, among others. However, it is well known that diagnostic analysis is a fundamental process in all statistical modeling for any data set.…”

Using RPM – GMM for TPS Interpolation in MCC Algorithm

“…From this problem, the authors made improvements to the TPS to reduce the number of control points while ensuring the accuracy of the classification results. The TPS iteration improvement studies have been published by the study authors in articles [7], [8], [9], [10], [11], [12] that when Reducing the number of TPS control points will be more efficient and smoother, the results are proven with different data sets.…”