Abstract
Bare earth extraction is an important component to light detection and ranging (LiDAR) data analysis in terms of terrain classification. The challenge in providing accurate digital surface models is augmented when there is diverse topography within the data set or complex combinations of vegetation and built structures. Few existing algorithms can handle substantial terrain diversity without significant editing or user interaction. This effort presents a newly developed methodology that provides a flexible, adaptable tool capable of integrating multiple LiDAR data attributes for an accurate terrain assessment. The terrain extraction and segmentation (TEXAS) approach uses a third-order spatial derivative for each point in the digital surface model to determine the curvature of the terrain rather than rely solely on the slope. The utilization of the curvature has shown to successfully preserve ground points in areas of steep terrain as they typically exhibit low curvature. Within the framework of TEXAS, the contiguous sets of points with low curvatures are grouped into regions using an edge-based segmentation method. The process does not require any user inputs and is completely data driven. This technique was tested on a variety of existing LiDAR surveys, each with varying levels of topographic complexity.
Highlights
Light detection and ranging (LiDAR) has become one of the most significant contributors to the production of usable and accurate surface data primarily due to the technology’s ability to produce digital elevation models with centimeter-level accuracy
In order to provide consistency with regard to the performance of terrain extraction and segmentation (TEXAS), all of the results presented for the selected subsets are digital terrain model (DTM) directly produced through a single, automated run of the software
Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Applied-Remote-Sensing on 08 Nov 2021 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use show the input digital surface model (DSM) on the left and the TEXAS output DTM on the right, they consist of some elevation analysis transects along the bottom of the image
Summary
Light detection and ranging (LiDAR) has become one of the most significant contributors to the production of usable and accurate surface data primarily due to the technology’s ability to produce digital elevation models with centimeter-level accuracy. The success of surface classification is undoubtedly dependent most on the accuracy of the digital terrain model (DTM), or bare earth component. The DTM is the fundamental layer of underlying topography which allows for the characterization/ classification of vegetation and man-made structures, and can assist in the derivation of human activity layers, obstruction determination, trafficability, floodplain mapping, and natural resource allocation
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