A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy

Abstract In recent years, many airborne point clouds filtering methods have been developed. However, it is still challenging for distinguishing ground and non-ground points in forested areas due to the rugged terrains, dense vegetation canopy and low-level penetration of laser pulses. To derive satisfactory filtering results, this paper proposed a mean shift segmentation morphological filter. In this method, the mean shift segmentation is used for acquiring object primitives to determine filtering window sizes automatically. The point clouds detrending is proposed for improving the adaptability towards sloped terrains. A point cloud shifting in x and y directions technique is developed to acquire more ground seeds for generating a more accurate trending surface. Finally, the filtered ground points by the progressive morphological filter are recovered by adopting the surface-based filtering strategy. The proposed method is tested and validated using 14 samples with different forested environments. Experimental results show that the proposed method can achieve the average total error of 1.11%. The kappa coefficients of all these 14 samples are larger than 90% and the average kappa coefficient is 96.43%. The average root mean square error (RMSE) of the proposed method is 0.63. All these indicators are the best when compared to some other famous filtering methods.