Brief DSM Classification Literature Review
Sea Level Rise Impacts on Gasline Infrastructure
Will Fourt
8/13/13
This document provides a brief overview on the literature surrounding the
classification or segmentation of LiDAR points into either ground and non-ground
points, or further into distinct feature categories such as trees, roads and buildings.
The reason for this review is to understand past methods in order to determine the
best method to classify tree points so they can be excluded from the DSM.
The majority of the literature about classifying LiDAR points is concerned with
identifying non-bare-earth points in order to exclude them when generating a highresolution digital elevation model (DEM) or digital terrain model (DTM). These
studies and methods are grouped in the “Ground Filtering Methods” section.
Other studies are concerned with further classifying las points into more distinct
categories such as buildings and vegetation. A large portion of the literature is
dedicated to doing this classification in order to classify building points to construct
3D models of buildings in urban environments. A smaller portion of the literature is
concerned with identifying vegetation points for applications such as species
identification and biometrics. For either purpose, vegetation points must be
distinguished, and are typically identified by the generally larger variation in local
heights associated with vegetation as opposed to other aboveground features.
These studies are grouped into the “Feature Classification Methods” section.
Overview Articles
First, the following review articles were helpful in grouping types of classification
methods.
a) (Charaniya, Manduchi, and Lodha 2004)
This article includes a background section that is useful in its
summary and categorization of literature on feature classification of
LiDAR data.
b) Meng, Currit, and Zhao 2010
This review article presents an overview of the many methods for
filtering lidar data.
Ground Filtering Methods
These techniques comprise most of the Lidar classification literature, and strive to
classify las points into “ground” and “non-ground” points only. The types of nonground, or non-bare-earth points do not need to be classified, as they are excluded
from the primary function of creating a DEM from the lidar data.
a) Lowest Elevation
i) (National Oceanic and Atmospheric Administration Coastal Services
Center 2013)
This NOAA study explains the use of software developed by the
International Hurricane Research Center (IHRC) at Florida
International University. This software, called Airborne LIDAR Data
Processing and Analysis Tools (ALDPAT), includes a number of
different filtering methods, but the one described in the paper is
based on using the lowest-elevation point within a certain
neighborhood.
ii) (Zhang and Whitman 2005)
This study compares the results of three types of ground filtering
algorithms in three different terrain types. The conclusion is that they
all work best in distinct types of terrain environments.
b) Maximum Slope Classification or “Ground Surface Steepness”
i) (Vosselman 2000)
Points with a slope to adjacent points above the specified threshold
are classified as non-ground.
ii) (Zhang and Whitman 2005)
This study compares the results of three types of ground filtering
algorithms in three different terrain types. The conclusion is that they
all work best in distinct types of terrain environments.
c) Surface Elevation Difference
d) Ground Surface Homogeneity – Using a Morphological Filter
i) (Zhang et al. 2003)
Progressively larger morphological filters are applied (combining
erosion and dilation) to identify objects above an elevation difference
threshold to be removed.
ii) (Zhang and Whitman 2005)
This study compares the results of three types of ground filtering
algorithms in three different terrain types. The conclusion is that they
all work best in distinct types of terrain environments.
e) Height-Bin Classification
i) (Raber et al. 2002)
This study uses the height bin histograms derived directly from the
lidar point-cloud to classify vegetation types using a supervised
classification. This vegetation map is then used to classify las points
by vegetation type, which can then be excluded when creating a DEM.
f) Contour-based Filters
i) (Hug, Krzystek, and Fuchs 2004)
LASTools uses a contour-based segmentation of las points where
contours are derived from sets of las points, and connected contours
below a threshold size are identified as objects above the ground
surface.
Feature classification methods
a) Height Texture
i) (Maas 1999)
Using the variation and texture of heights of lidar points within each
neighborhood, points were classified with 98% accuracy.
ii) (Alharthy and Bethel 2002)
Using the difference between heights in first and last returns, the root
mean square error was calculated for neighborhoods of points. High
root mean square errors accurately indicated vegetated features.
iii) (Elberink and Maas 2000)
This study uses texture, or variation in local heights of lidar points to
distinguish between vegetation and man-made above ground
features.
b) Surface Clustering
i) (Filin 2002)
Local clusters of similar values for slope and height difference
between points are grouped to classify four groups: forested/wooded
areas, low vegetation areas and rough surfaces, smoothly varying
topography, and planar surfaces.
ii) (Filin and Pfeifer 2006)
This study uses an alternative neighborhood system for clustering
points based on the density and distribution of points for the
definition of a neighborhood.
c) Intensity
i) (Song et al. 2002)
This study uses the intensity associated with each lidar point to
classify points into four classes: asphalt road, grass, house roofs, and
trees. After applying noise-removal filtering and resampling
techniques, intensity was successfully used to classify points into
these four categories.
ii) (Donoghue et al. 2007)
This study uses the intensity of lidar points to differentiate tree
species in mixed-species forest plots.
d) Combination of Structure and Instensity
i) (Charaniya, Manduchi, and Lodha 2004)
This study classified points into four classes: grass, rooftops, trees,
and roads, using a supervised classification of five lidar attributes:
normalized height, height variation, difference in height between first
and last return, luminance (includes non-visible spectrum), and
intensity (includes visible spectrum).
Bibliography
Alharthy, Abdullatif, and James Bethel. 2002. “Heuristic Filtering and 3D Feature
Extraction from LIDAR Data.” International Archives of Photogrammetry
Remote Sensing and Spatial Information Sciences 34 (3/A): 29–34.
Charaniya, A.P., R. Manduchi, and S.K. Lodha. 2004. “Supervised Parametric
Classification of Aerial LiDAR Data.” In Conference on Computer Vision and
Pattern Recognition Workshop, 2004. CVPRW ’04, 30–30.
doi:10.1109/CVPR.2004.172.
Donoghue, Daniel N.M., Peter J. Watt, Nicholas J. Cox, and Jimmy Wilson. 2007.
“Remote Sensing of Species Mixtures in Conifer Plantations Using LiDAR
Height and Intensity Data.” Remote Sensing of Environment 110 (4) (October
30): 509–522. doi:10.1016/j.rse.2007.02.032.
Elberink, Sander Oude, and Hans-Gerd Maas. 2000. “The Use of Anisotropic Height
Texture Measures for the Segmentation of Airborne Laser Scanner Data.”
International Archives of Photogrammetry and Remote Sensing 33 (B3/2;
PART 3): 678–684.
Filin, Sagi. 2002. “Surface Clustering from Airborne Laser Scanning Data.”
International Archives of Photogrammetry Remote Sensing and Spatial
Information Sciences 34 (3/A): 119–124.
Filin, Sagi, and Norbert Pfeifer. 2006. “Segmentation of Airborne Laser Scanning
Data Using a Slope Adaptive Neighborhood.” ISPRS Journal of
Photogrammetry and Remote Sensing 60 (2) (April): 71–80.
doi:10.1016/j.isprsjprs.2005.10.005.
Hug, C., P. Krzystek, and W. Fuchs. 2004. “Advanced Lidar Data Processing with
LasTools.” International Archives of Photogrammetry and Remote Sensing 35.
http://www.cartesia.org/geodoc/isprs2004/comm2/papers/240.pdf.
Maas, Hans-Gerd. 1999. “The Potential of Height Texture Measures for the
Segmentation of Airborne Laserscanner Data.” In Fourth International
Airborne Remote Sensing Conference and Exhibition/21st Canadian
Symposium on Remote Sensing, 154–161.
http://www.lr.tudelft.nl/fileadmin/Faculteit/LR/Organisatie/Afdelingen_en
_Leerstoelen/Afdeling_RS/Optical_and_Laser_Remote_Sensing/Research/Res
earch_Fields/Airborne_laser_scanning/doc/Maasthepotential.pdf.
Meng, Xuelian, Nate Currit, and Kaiguang Zhao. 2010. “Ground Filtering Algorithms
for Airborne LiDAR Data: A Review of Critical Issues.” Remote Sensing 2 (3)
(March 22): 833–860. doi:10.3390/rs2030833.
National Oceanic and Atmospheric Administration Coastal Services Center. 2013.
“Topography and Bathymetry Data Considerations: Refinement of
Topographic Lidar to Create a Bare Earth Surface.” Accessed August 12.
http://csc.noaa.gov/digitalcoast/_/pdf/Refinement_of_Topographic_Lidar_to
_Create_a_Bare_Earth_Surface.pdf.
Raber, George T., John R. Jensen, Steven R. Schill, and Karen Schuckman. 2002.
“Creation of Digital Terrain Models Using an Adaptive Lidar Vegetation Point
Removal Process.” Photogrammetric Engineering and Remote Sensing 68
(12): 1307–1314.
Song, Jeong-Heon, Soo-Hee Han, K. Y. Yu, and Yong-Il Kim. 2002. “Assessing the
Possibility of Land-cover Classification Using Lidar Intensity Data.”
International Archives of Photogrammetry Remote Sensing and Spatial
Information Sciences 34 (3/B): 259–262.
Vosselman, George. 2000. “Slope Based Filtering of Laser Altimetry Data.” IAPRS
XXXIII.
http://www.academia.edu/1869144/Slope_based_filtering_of_laser_altimetr
y_data.
Zhang, Keqi, Shu-Ching Chen, D. Whitman, Mei-Ling Shyu, Jianhua Yan, and Chengcui
Zhang. 2003. “A Progressive Morphological Filter for Removing Nonground
Measurements from Airborne LIDAR Data.” IEEE Transactions on Geoscience
and Remote Sensing 41 (4): 872–882. doi:10.1109/TGRS.2003.810682.
Zhang, Keqi, and Dean Whitman. 2005. “Comparison of Three Algorithms for
Filtering Airborne Lidar Data.” Photogrammetric Engineering & Remote
Sensing 71 (3): 313–324.