Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain

• Published in: Remote sensing of environment Vol. 108; no. 1; pp. 9 - 23
• Main Authors: Kobler, Andrej, Pfeifer, Norbert, Ogrinc, Peter, Todorovski, Ljupčo, Oštir, Krištof, Džeroski, Sašo
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.05.2007; Elsevier Science
• Subjects: Airborne laser scanning; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Digital terrain modeling; Earth sciences; Earth, ocean, space; Exact sciences and technology; Forest; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Lidar; Teledetection and vegetation maps; Lidar; Forest; Airborne laser scanning; Digital terrain modeling; algorithms; Scanner; laser methods; data; terrains; vegetation; outliers; Cloud point; tin; Data structure; filtering; Vector; Multiple; Forest zone; Redundancy; topography; forests; samples; Generation; interpolation; Filter; Individual; Laser; digital terrain models; Problem
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2006.10.013

We present a new algorithm for digital terrain model (DTM) generation from an airborne laser scanning point cloud, called repetitive interpolation (REIN). It is especially applicable in steep, forested areas where other filtering algorithms typically have problems distinguishing between ground returns and off-ground points reflected in the vegetation. REIN can produce a DTM either in a vector grid or in a TIN data structure. REIN is applied after an initial filtering, which involves removal of all negative outliers and removal of many, but not necessarily all, off-ground points by some existing filtering algorithm. REIN makes use of the redundancy in the initially filtered point cloud (FPC) in order to mitigate the effect of the residual off-ground points. Multiple independent random samples are taken from the initial FPC. From each sample, ground elevation estimates are interpolated at individual DTM locations. Because the lower bounds of the distributions of the elevation estimates at each DTM location are almost insensitive to positive outliers, the true ground elevations can be approximated by adding the global mean offset to the lower bounds, which is estimated from the data. The random sampling makes REIN unique among the methods of filtering airborne laser data. While other filters behave deterministically, always generating a filter error in special situations, in REIN, because of its random aspects, these errors do not occur in each sample, and typically cancel out in the final computation of DTM elevations. Reduction of processing time by parallelization of REIN is possible. REIN was tested in a test area of 2 hectares, encompassing steep relief covered by mixed forest. An Optech ALTM 1020 lidar was used, with a flying height of 260–300 m above the ground, the beam divergence was 0.3 mrad, and the obtained point cloud density for the last returns was 8.5 m − 2 . A DTM grid was generated with 1 m horizontal resolution. The root mean square elevation error of the DTM ranged between ± 0.16 m and ± 0.37 m, depending on REIN sampling rate and number of samples taken, the lowest value achieved with 4 samples and using a 23% sampling rate. The paper also gives a short overview on existing filtering algorithms.