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A COMPARISON OF TWO DIFFERENT APPROACHES OF POINT CLOUD
CLASSIFICATION BASED ON FULL-WAVEFORM LIDAR DATA
Jin-hu Wang *^ *, Chuan-rong Li, Ling-li Tang*, Mei Zhou", Jing-mei Li *
* Academy of Opto-Electronics, Chinese Academy of Sciences. Dengzhuang South Road, Beijing
5 The Graduate University of Chinese Academy of Sciences, Yuquan Road, Beijing. glancefox@gmail.com
Commission III, WG III /2
KEY WORDS: full-waveform LiDAR; Decomposition, Unsupervised classification, point cloud, space transform, EM algorithm
ABSTRACT:
In this paper, two different point cloud classification approaches were applied based on the full-waveform LiDAR data. At the
beginning, based on the full-waveform LiDAR data, we decomposed the backscattered pulse waveform and abstracted each
component in the waveform after the pre-processing of noise detection and waveform smoothing. And by the time flag of each
component acquired in the decomposition procedure we calculated the three dimension coordination of the component. Then the
components’ waveform properties, including amplitude, width and cross-section, were uniformed respectively and formed the
Amplitude/Width/Section space. Then two different approaches were applied to classify the points. First, we selected certain targets
and trained the parameters, after that, by the supervised classification way we segmented the study area point. On the other hand, we
apply the IHSL colour transform to the above space to find a new space, RGB colour space, which has a uniform distinguishability
among the parameters and contains the whole information of each component in Amplitude/Width/Section space. Then the fuzzy C-
means algorithm is applied to the derived RGB space to complete the LiDAR point classification procedure. By comparing the two
different segmentation results, which may of substantial importance for further targets detection and identification, a brief discussion
and conclusion were brought out for further research and study.
1. INTRODUCTION
Airborne Laser Scanning (ALS) is an active remote
sensing technique providing direct range measurements between
laser scanner and objects, has witnessed an alternative source
for acquisition of ranging data in last decade. Range is
determined directly from the signal runtime measurements. And
airborne LiDAR deliver fast and reliable representation of
landscape by Georeferencing. Depending on the geometry of
illuminated surfaces, several backscattered echoes can be
recorded for a single pulse emission. This showed the potential
of multi-echo LiDAR data for urban area analysis and building
extraction (Frueh et al., 2005). While many others study the
LiDAR backscattered pulse intensity (Charaniya et al., 2004)
and combine LiDAR and multispectral data(Secord et al., 2006)
for classification. Since 2004, new commercial ALS systems
called full-waveform (FW) LiDAR have emerged with the
ability to record the complete waveform of the backscattered
1D-signal. Each echo in this signal corresponds to an
encountered object. Thus, in addition to range measurements,
further physical properties of objects included in the diffraction
cone may be revealed by analysing the shape of backscattered
waveforms. A detailed state-of-the-art on full-waveform
topographic LiDAR can be found in Mallet et al., 2009. In
urban scenes, the potential of such data has been barely
investigated, in addition to the geometry to detect vegetation
areas (Gross et al., 2007; Wagner et al., 2008).
In this paper, two different point cloud classification
approaches were applied based on the full-waveform LiDAR
data. Firstly, the backscattered full-waveform LiDAR pulse
waveform was decomposed and all components in the
waveform were abstracted. Further, by the time flag of each
component acquired in the decomposition procedure, three
dimension coordinates of the components were calculated. Also,
* Corresponding author.
the components’ waveform properties, including amplitude,
width and cross-section, were uniformed respectively to form
the Amplitude/Width/Section space. After that two different
approaches were applied to classify the points. On one hand,
region of interest were selected and samples were trained to
perform supervised classification. On the other hand, IHSL
colour transform was introduced to transform the above space to
find a new RGB colour space. Afterwards, the fuzzy C-means
algorithm was applied to complete the LiDAR point
classification procedure. By comparing the two different
segmentation results, which may of substantial importance for
further targets detection and identification, a brief discussion
and conclusion were brought out for further research and study.
2. WAVEFORM DECOMPOSITION
In order to come to an analytical waveform properties
solution, assuming that the scattering properties of a cluster of
scatters can be described by Generalized Gaussian Function.
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20
where $ is amplitude and O the standard deviation of the
cluster. The cluster position is specified by 4/ while QX denote
the shape of the component waveform. The targets
backscattered pulse waveform is the superposition of echoes
from scatters at different ranges. To abstract every components’
parameters contained in backscattered waveform, the
Expectation Maximum algorithm is applied to accomplish the
decomposition process. The EM algorithm was presented by
Dempster. Laind and Rubin in 1977 to estimate the parameters
