Abstract
A fast and robust interpolation filter based on finite difference TPS has been proposed in this paper. The proposed method employs discrete cosine transform to efficiently solve the linear system of TPS equations in case of gridded data, and by a pre-defined weight function with respect to simulation residuals to reduce the effect of outliers and misclassified non-ground points on the accuracy of reference ground surface construction. Fifteen groups of benchmark datasets, provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) commission, were employed to compare the performance of the proposed method with that of the multi-resolution hierarchical classification method (MHC). Results indicate that with respect to kappa coefficient and total error, the proposed method is averagely more accurate than MHC. Specifically, the proposed method is 1.03 and 1.32 times as accurate as MHC in terms of kappa coefficient and total errors. More importantly, the proposed method is averagely more than 8 times faster than MHC. In comparison with some recently developed methods, the proposed algorithm also achieves a good performance.
Highlights
With an efficient collection of high-resolution 3D information of the Earth’s surface, airborne light detection and ranging data have been widely used in many applications, such as construction of digital elevation models (DEMs) [1], forest inventory [2, 3] and animal distribution simulation [4]
Results demonstrate that only using the smoothness effect cannot completely avoid misclassification of non-ground points, such as those marked by the rectangles (Fig 11)
To improve the computational efficiency of present Thin plate spline (TPS)-based interpolation filters, a fast and robust filter based on finite difference TPS computation was developed in this paper
Summary
With an efficient collection of high-resolution 3D information of the Earth’s surface, airborne light detection and ranging (lidar) data have been widely used in many applications, such as construction of digital elevation models (DEMs) [1], forest inventory [2, 3] and animal distribution simulation [4]. Since raw lidar data contains a large volume of points acquired from different objects [5, 6], it is necessary to differentiate ground and non-ground points. Many filtering algorithms have been proposed to extract ground points from raw point clouds. These methods can be categorized into three main groups [7,8,9]: slopebased, morphological-based and interpolation-based filters. Slope-based methods are based on the assumption that two nearby points should have a small height difference. If the slope of two nearby points is larger than a predefined threshold, the higher elevation point is classified as the non-ground point [10].
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