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Abstract
Over the past decades, plenty of filtering algorithms have been presented to distinguish ground and non-ground points from airborne LiDAR point clouds. However, with the existing methods, it is difficult to derive satisfactory filtering results on rugged terrains with dense vegetation due to the low-level penetration ability of laser pulses. Therefore, a multi-level interpolation-based filter is developed in this paper. The novelty of the algorithm lies within its usage of multi-scale morphological operations and robust z-score to correctly select ground seeds as more as possible, and a terrain-adaptive residual threshold to adapt to various terrain characteristics. Rural samples provided by International Society for Photogrammetry and Remote Sensing (ISPRS) were employed to assess the performance of the proposed method and its results were compared with 15 filtering algorithms developed in recent 5 years (2015-2019). Results show that the proposed method with the optimized parameters produces the best accuracy with the average total error and kappa coefficient of 1.89% and 87.88%, respectively. We further filtered high-density point clouds in six forested areas with different vegetation covers and terrain slopes. Results demonstrate that the proposed algorithm is more accurate than the well-known filtering methods including morphological-based filter, progressive TIN densification filter (PTD), improved PTD and cloth simulation filter, with the average total error decreased by 26.2%, 19.9%, 3.8% and 40.4%, respectively. Moreover, the DEMs of the proposed method have lower average root mean square errors than the four classical filters. Therefore, the proposed method can be considered as an effective ground filtering algorithm for airborne LiDAR point clouds in forested areas.
Highlights
Nowadays, airborne light detection and ranging (LiDAR) with the ability of tree canopy penetration and little influence by shadows and surrounding light conditions has become a valuable and effective tool in forest inventory, such as individual-tree based height determination and volume estimation, and tree-species classification [1]–[3]
International Society for Photogrammetry and Remote Sensing (ISPRS) DATASET To assess the average filtering performance, the 6 ISPRS samples were filtered by the proposed method with one single set of parameters
Through trial-and-error, the best parameter settings are obtained in terms of the minimum average kappa coefficient (Table 5)
Summary
Airborne light detection and ranging (LiDAR) with the ability of tree canopy penetration and little influence by shadows and surrounding light conditions has become a valuable and effective tool in forest inventory, such as individual-tree based height determination and volume estimation, and tree-species classification [1]–[3]. The extraction of ground points from raw airborne LiDAR point clouds is the pre-requisite and most important step for almost all LiDAR applications This process is termed as filtering [4]. According to their working principles, the existing filtering algorithms can be divided into six broad categories: slope-based, morphology-based, interpolation-based, statistical-based, machine learningbased and segmentation-based approaches [5], [6]. These filters achieve a good performance in relatively flat terrains with simple landscapes, but show poor results in mountainous terrains, cliffs, ridges and discontinuities with complex environments [7]. Compared to the other filtering methods, the interpolation-based filters commonly obtain better results since they can use more contexts [5]; they cannot perform well in dense forested areas, because the canopy prevents most laser pulses from reaching the ground surface, resulting in few ground points beneath the
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