AUTOMATIC EXTRACTION OF BUILDING ROOFS FROM PICTOMETRY’S
ORTHOGONAL AND OBLIQUE IMAGES
Yandong Wang
Pictometry International Corp.
Suite A, 100 Town Centre Dr., Rochester, NY14623, the United States
vandong wang@pictometrv.com
Commission III, WG III/4
KEY WORDS: Photogrammetry, Extraction, Building, Image, Edge, Automation.
ABSTRACT:
Automatic extraction of buildings from digital images aims at detection of buildings from digital images and reconstruction of roof
structure automatically. At Pictometry, more than 30 million images are captured every year and how to extract the useful
information of objects on the ground from the existing image library for various applications is a big challenge we face now. In this
paper, an automatic approach for extraction of building roof from digital orthogonal and oblique imagery is proposed. The proposed
method uses image processing technique to derive the accurate 3D structure of building roof for accurate roof measurement, 3D
modeling, computation of building footprints, etc. It consists of three major steps, i.e. extraction of roof corner and ridge points from
the images, automatic matching of roof corner and ridge points between orthogonal and oblique images and grouping of the matched
roof points to create roof facets. In this study, the modified Moravec operator is used to extract feature points from digital images. To
find roof points which cannot be extracted by the point extractor, edge information is also extracted. Due to the nature of roof points,
especially corner points and the difference between orthogonal and oblique images, a feature-based image matching technique is
used to derive 3D information of roof corner and ridge points, instead of area-based matching. To match roof points correctly, edges
associated with a corner or ridge point and their properties are used. After 3D roof points are generated, roof points belonging to the
same roof facet are grouped together by using their spatial relations. Once points belonging to the same facet are found, a surface is
fitted to the points and outliers can be removed during this process.
1. INTRODUCTION
3D modeling of buildings has many applications in different
areas such as 3D city modeling, communication, insurance,
urban planning, etc. Automatic extraction of buildings from
digital images has received significant attentions from both
computer vision and photogrammetry over decades and there
are a number of methods developed by researchers in these
fields. The early research was focused on the extraction of
buildings with simple structures (rectangular shape) (Huertas
and Nevatia, 1988; Fua and Hanson, 1991; Dang et al, 1994;
Roux and McKeown, 1994; Lin et al, 1995). Some approaches
dealing with buildings with complex structures were developed
in Lang and Fórstner (1996), Henricsson (1996) and Taillandier
and Deriche (2004). Recently an approach for extraction of
building facade has been developed by Xiao et al (2010).
Pictometry started to capture geo-referenced imagery using its
proprietary imaging system more than a decade ago. Currently
more than 30 million of both vertical and oblique images are
captured every year at Pictometry and the number still increases
every year. One big challenge we face is how to extract useful
information from the existing image library for different users.
In this paper, an approach for automatic extraction of buildings
from both vertical and oblique imagery is presented. The
approach focuses on the reconstruction of building roof using
image processing methods for different applications such as 3D
city modeling, insurance and urban planning. It consists of
three major steps, i.e. automatic extraction of features and
generation of topological relations between features, matching
of 2D features to derive 3D features and grouping of 3D
features to generate roof facets. In the following sections, a
building model for automatic building extraction will be
described in section 2. The details of feature extraction,
matching and grouping will be given in section 3. Some testes
results will be given and discussed in section 4.
2. BUILDING MODEL AND EXTRACTION
STRATEGY
Various building models have been developed for building
extraction based on the type of buildings to be extracted and the
resolution of images to be used. They range from using simple
geometric constraints such as rectangular shape to the use of
complex 3D geometric constraints for extraction of buildings
with complex structures. Extraction strategy largely relies on
the model to be used for building extraction. It can be data-
driven, model-driven or hybrid. In a data-driven strategy,
extraction usually starts from extraction of generic features
from imagery such as points and lines. 3D features are
generated by matching 2D features from overlapping images
and buildings are reconstructed by grouping 3D features. À
review on building models, building extraction strategy and
performance of some existing methods can be found in Mayer
(1999).
The model used in this study uses 3D geometric constraints. It
is assumed that a residential building roof consists of a number
of facets which connect to each other. Each roof facet has a
number of roof points which are connected by roof lines such
as eaves, ridge and valley lines. A bottom-up strategy will be
used for reconstruction of building roofs in this study. It starts
with the extraction of point and line features from 2D imagery
prir
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