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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
HIERARCHICAL ALGORITHM IN DTM GENERATION AND AUTOMATIC
EXTRACTION OF ROAD FROM LIDAR DATA
Li Hui-ying®, Xu Yu-jun *, Wang Zhi?, Lu Yi-nan^
* College of Computer Science and Technology, Jilin University, Qianjin Str.2699, Changchun City, 130012, China
- lihuiying(2)jlu.edu.cn
? College of Resources and Civil Engineering, Northeastern University, Shenyang, 110819,China -
wangzhi@mail.neu.edu.cn
KEY WORDS: LIDAR, DTM, Road Extraction
ABSTRACT:
Growing demand for an efficient land use above and below the ground is motivating cadastre and land management systems to
move from traditional 2D systems toward three dimensional ones. Airborne laser technology offers direct acquisition of dense
and accurate 3D data. In order to get 3D road this paper proposes a hierarchical algorithm to extract terrain point from LIDAR
data. We stratify the raw LiDAR data according to the height, judge terrain points and non-terrain points by the connectivity. In
the case of road network, it indicates the morphological characteristics of network structure with a certain length continuous strip
and small difference in intensity. All these information, including elevation information, the intensity information, the
morphological characteristics and other local features, are used for extracting the road network from DTM. Local morphological
filtering method is implementing for finding clear boundaries and rich details of the road profile. Following the presentation of
the algorithm results for this approach are shown and evaluated
1. INTRODUCTION
Using the LiDAR(Light Detection And Ranging) technology,
we can obtain the 3-D information of the earth surface quickly
and accurately[1]. By contrast with traditional
photogrammetry, the 3D urban data capturing using LiDAR is
of higher speed, higher vertical accuracy and lower cost [2].
Road extraction from remotely sensed data is a challenging
issue and has been approached in many different ways by
Photogrammetric and digital image processors. Some of the
methods are quite complex and require the fusion of several
data sources or different scale space images. Most of the
existing road extraction algorithms use the method of
clustering or growth. Such as Jeong-Heon Song makes an
assessment of possibility with intensity data to classify the
point cloud[3] ,and Farhad Samadzadegan use classifier
fusion method [5]. Liang Gong uses K-means clustering
algorithm to detect roads based on intensity return and the 3D
information of LiDAR data [1]. But these methods only
consider the intensity value of LiDAR data. In practice, the
classification methods, which only use the intensity values,
are difficult to achieve accurate classification results. The
clustering methods often require pre-machine learning and the
different regions have different roads characteristics.
Therefore, the clustering methods have difficulties in
producing accurate extraction results of different types of
roads. Thus the clustering methods do not have broad
applicability. In the case of region growing approach, it is not
only has a great dependence on the seeds, but also high time
complexity. This paper describes a new method of the
automatic DTM generation and road extraction and tries to
overcome the lack of methods mentioned above.
2. HIERARCHICAL ALGORITHM
Generalization DTM from LiDAR data was often used to
extract 3D road. It computes the points cloud to access DTM,
then sets a certain threshold, and let point in point cloud
subtract DTM , if the value is greater than threshold then
delete it. This method cost high time complexity. This article
will use a hierarchical algorithm to divide the original data
according to the height. Through the judgement of empty
holes produced by stratification, rule out points unground.
For the purpose of this paper, we define LIDAR data point as
pi,
P; = (px,lpy,lpz,lpi) ()
where [px ; Ipy , and lpz represent the last pulse laser
strike 3D coordinates and lpi represents the intensity of the
point. Let S represents the set of all laser points,
S iD. pt (2)
The creation of the layer is defined by (3).
Sz, = {p, €S: jxAt < Pig, ÉO+UxA ©
where SZ, denotes the j-th layer , Pip: is the last pulse z
coordinate of p. is the number of the layer and Af is
the thickness of the layer. Then we get these layers as shown
in Figure 1.
Due to the change trend of the ground is slow and connect to
each other, it produces a large slice connected area (shown in
Figure 1) in a horizontal section. The large connection areas
are ground.
* Corresponding author. Lu Yi-nan ; Tel: +8613504330645;Email:luyn@jlu.edu.cn
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