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A ROBUST METHOD FOR FILTERING NON-GROUND MEASUREMENTS FROM
AIRBORNE LIDAR DATA
Fabio Crosilla, Domenico Visintini, Guido Prearo
Department of Geo-Resources & Territory, University of Udine, via Cotonificio, 114 1-33100 Udine, Italy
crosilla@dgt.uniud.it
KEY WORDS: LIDAR, DEM/DTM, Data, Surface, Detection, Algorithms
ABSTRACT:
This paper proposes a new filtering method of non-ground measurements from airborne LIDAR data through a Simultaneous
AutoRegressive (SAR) analytical model and exploiting a Forward Search (FS) algorithm (Atkinson and Riani, 2000, Cerioli and
Riani, 2003), a newly developed tool for robust regression analysis and robust estimation of location and shape.
In SAR models, with respect to classical spatial regression models, the correlation among adjacent measured points is taken into
account, by considering two quantities for the measured dataset: a coefficient of spatial interaction and a matrix of point adjacency
(binary digits for regular grids or real numbers for irregular ones).
FS approach allows a robust iterative estimation of SAR unknowns, starting from a subset of outlier-free LIDAR data, suitably
selected. The method proceeds in its iterative computations, by extending such a subset with one or more points according to their
level of agreement with the postulated surface model. In this way, worse LIDAR points are included only at the ending iterations.
SAR unknowns and diagnostic statistical values are continuously estimated and monitored: an inferentially significant variation of
the surface coefficients reveals as points included from now on can be classified as outliers or “non-ground” points.
The method has been implemented using Matlab® language and applied either to differently simulated LIDAR datasets or really
measured points, these last acquired with an Optech® ALTM 3033 system in the city of Gorizia (North-East Italy). For both kinds of
datasets the proposed method has very well modeled the ground surface and detect the non-ground (outliers) LIDAR points.
1. INTRODUCTION
Airborne Laser Scanning technique is extremely efficient to
fulfil increasing demand of high accuracy Digital Terrain or
Surface Models (DTM or DSM) for civil engineering,
environment protection, planning purposes, etc. But, if standard
procedures for acquiring Airborne Laser Scanning data have
already come nowadays a long way, on the other hand, the
choice of appropriate data processing techniques for different
particular applications is still being investigated. For this last
essential topic of research, several algorithms have been
developed for semi automatically/automatically extracting of
objects from bare terrain. But in general, their filtering
efficiency seems to vary very much with local conditions. In
fact, the quality of nearly all procedures too often depends on an
appropriate setting or determination of thresholds and control
values (Jacobsen et al, 2002, Kraus, 1997, Voelz, 2001).
Moreover, another important task not yet completely solved is
to simultaneously proceed to both filtering and generation of
DTM. For this last requirement, the filtering algorithm
presented throughout this paper manages not only to “remove”
additional features on ground such as buildings, vegetation etc.,
but even to generate DTMs with points classified as “ground”.
Looking thought the recent literature in LIDAR data filtering, a
significant number of techniques has been developed to remove
man-made “artefacts” on the territory, in order to obtain the true
Digital Terrain Model. Unfortunately, in order to completely
remove non-terrain data points, these techniques often require
interactive editing. This leads to increasing the production
times. Thus, there is yet great interest in developing effective
and reliable tools and algorithms on this topic.
Our research starts from the analysis of the most significant
techniques and algorithms present in literature; that is:
* Least squares interpolation (Kraus e Pfeifer, 1997): filter
out trees in forested areas by fitting an interpolating surface
to the data and using a weighted ground iterative least
squares scheme to bring down the contribution of points
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above the surface, so that it gets closer and closer to the
lowest data points. À similar approach is used to filter out
also buildings (Rottensteiner et al., 2002).
e Erosion/dilatation functions in mathematical morphology
(Zhang et al., 2003): starting from an initial subset of points
and by gradually increasing the window size of the filter
using elevation difference thresholds, data of vehicles,
vegetation, and buildings are removed, while ground data
are preserved. Such points are then included in a DTM.
e Slope based functions (Vosselman, 2003): slope based
filtering operates using mathematical morphology, and
fixing a slope threshold. This, being the maximum allowed
height difference between two points, is expressed as a
function of the distance between different terrain points.
e TIN densification (Axelsson, 2000): an adaptive TIN model
born to find ground points in urban areas. Initially seed
ground points within a user defined grid of a size greater
than the largest non ground features are selected to compose
an initial ground dataset. Then, one point above each TIN
facet is added to the ground dataset at each iteration if its
parameters are below specific threshold values. Different
thresholds have to be given for various land cover types.
e Application of Spline functions (Brovelli et al, 2002):
through a least squares approach with Tikhonov
regularization, non-terrain points are filtered out by
analyzing residuals from a spline interpolation.
This paper proposes instead a new stochastic approach for
filtering, based on the following spatial regression model.
2. SIMULTANEOUS AUTOREGRESSIVE (SAR)
MODELS FOR SPATIAL FILTERING
The analytical models called as SAR (Simultaneous Auto
Regressive, Whittle, 1954) belong to a class of algorithms
largely used in many fields for describing spatial variations.
