ABSTRACT:
assumed ideal circular shaped trees in the reference data.
1. INTRODUCTION
The need for accurate extraction of urban objects such as
buildings, trees and roads is highly growing due to its vital role
in different applications such as urban planning, civil
engineering, and environment protection. The continuous
advancements of the airborne LIDAR and image sensors offered
new enhanced data specifications that need evolving processing
techniques to exploit the new abilities, resolutions, and
accuracies in highly automatic fashion to overcome the slow
and costly but yet accurate human processing. The emerging
LIDAR technology complements the aerial imagery technology
towards more complete and accurate sensing to enhance the
automatic extraction process. LIDAR data lack the semantically
rich data provided by the different bands of the optical images
Which is very useful for the detection of many classes such as
vegetation. Also, optical images typically offer higher
resolution data than LIDAR. However, despite the sophisticated
approaches of image processing, feature extraction and
matching, automatic DTM generation using optical images is
struggling against several problems such as occlusions,
shadows, and steep slopes. These problems, on the other hand,
can be obviously reduced using LIDAR technology that offers
reliable height data regardless of objects textures and
illumination conditions. LIDAR also plays an important role in
deriving ortho-photos from aerial photos. The effectiveness of
LIDAR is very noticeable due to its level of accuracy and its
highly automated data acquisition workflow.
A wide range of approaches have been developed to employ
LIDAR data in land cover classification tasks. Several iterative
methods have been proposed to filter the non-terrain points out
A NEW OBJECT BASED METHOD FOR AUTOMATED EXTRACTION OF URBAN
OBJECTS FROM AIRBORNE SENSORS DATA
A. Moussa *', N. El-Sheimy *
* Dept. of Geomatics Engineering, University of Calgary, 2500 University Drive NW, Calgary AB, T2N 1N4 Canada —
(amelsaye, elsheimy)@ucalgary.ca
Commission III, WG III/4
KEY WORDS: Classification, LIDAR, Aerial, Image, Segmentation, Urban, Vegetation, Building
The classification of urban objects such as buildings, trees and roads from airborne sensors data is an essential step in numerous
mapping and modelling applications. The automation of this step is greatly needed as the manual processing is costly and time
consuming. The increasing availability of airborne sensors data such as aerial imagery and LIDAR data offers new opportunities to
develop more robust approaches for automatic classification. These approaches should integrate these data sources that have different
characteristics to exceed the accuracy achieved using any individual data source. The proposed approach presented in this paper fuses
the aerial images data with single return LIDAR data to extract buildings and trees for an urban area. Object based analysis is
adopted to segment the entire DSM data into objects based on height variation. These objects are preliminarily classified into
buildings, trees, and ground. This primary classification is used to compute the height to ground for each object to help improve the
accuracy of the second phase of classification. The overlapping perspective aerial images are used to build an ortho-photo to derive a
vegetation index value for each object. The second phase of classification is performed based on the height to ground and the
vegetation index of each object. The proposed approach has been tested using three areas in the centre of the city of Vaihingen
provided by ISPRS test project on urban classification and 3D building reconstruction. These areas have historic buildings having
rather complex shapes, few high-rising residential buildings that are surrounded by trees, and a purely residential area with small
detached houses. The results of the proposed approach are presented based on a reference solution for evaluation purposes. The
classification evaluation exhibits highly successful classification results of buildings class. The proposed approach follows the exact
boundary of trees based on LIDAR data which provide above average classification results for the trees when compared to the
such as successive spline interpolation using gradient and
surface orientation analysis (Brovelli et al., 2002), fitting an
interpolating surface using iterative least squares (Kraus et al.,
1998), iterative densification of a triangular irregular network
(TIN) (Axelsson, 2000). Clustering algorithms such as k-means
(Chehata et al., 2008), and fuzzy c-means (Zulong et al., 2009)
have been proposed to cluster the LIDAR points into different
classes. Geometric descriptors such as static moments,
curvature, and data anisotropy have been used by (Roggero,
2002) for clustering LIDAR data. (Song et al., 2002) assessed
the possibility of using LIDAR intensity data for land-cover
classification. (Parrish, 2008) have utilized wavelet analysis to
detect vertical objects and classify buildings from LIDAR data
points. (Filin, 2002) have used connectivity and principal
component analysis to cluster LIDAR data in surface categories.
On the other hand, many approaches have been proposed to
perform the classification task using aerial images. These
approaches exhibit different features, models, and classifiers to
accomplish the classification task. Several texture features have
been used as an input to the classification stage, such as Gabor
filter (Baik et al., 2004), fractal dimension and coefficient of
variation (Solka et al., 1998), and Non Subsampled Contourlet
Transform NSCT (Wei et al, 2010). The pixel color
components have been used directly as the input to the classifier
(Mokhtarzade et al., 2007). Both texture and color features have
been used together for classification (Haim et al., 2006). A wide
variety of classification algorithms have been employed, such as
Naive Bayes classifier (Maloof et al., 2003), fuzzy logic
(Sheng-hua et al., 2008), Neural Networks (Mokhtarzade et al.,
2007), Support Vector Machine SVM algorithm (Corina et al.,
2008).
