Classification of basic roof types based on VHR
optical data and digital elevation model
Silvia Valero1 , Jocelyn Chanussot1 & Philippe Gueguen2
1 GIPSA-Lab, Grenoble Institute of Technology, France
2 LGIT, Grenoble - LCPC, Paris - France
[2] [3]. Some operators can also be derived using
morphological operators [4]. Other strategies are mostly
based on DEM images [5]. Finally, some methods
combine optical image information and altitude data [?].
Further detailing this initial (far from trivial) step lies
beyond the scope of this paper. Let us assume that the
output of this step (i.e. the input of the proposed study)
is the set of all the individual segmented buildings.
2) Feature extraction. The proposed technique analyzes
different skeleton features using as input data the roofs
extracted in the first step. To obtain the final roof-type,
two different parameters are calculated. This algorithm
is described in the next Section II.
3) Classification. The proposed pattern recognition is
analysed in Section III.
Abstract—In the frame of seismic vulnerability assessment in
urban areas, it is very important to estimate the nature of the
roof of every building and, in particular, to make the difference
between flat roofs and gable ones. In order to perform this tedious
task automatically on a large scale, remote sensing data provide
a useful solution. In this study, we use simultaneously very high
resolution panchromatic data, and an accurate digital elevation
model. The fusion of these two modalities enables the extraction
of two mixed features. Based on these features the classification
between the two considered classes becomes a simple linearly
separable problem.
Index Terms—Data Fusion, Seismic Risk Assessment, Mathematical Morphology, Skeleton , Classification, Urban Areas
I. I NTRODUCTION
In the frame of seismic vulnerability assessment in urban
areas, estimating the nature of every building’s roof is a
key issue. In this paper, we present a feature extraction
process aiming at discriminating between flat roofs and gable
ones. We consider incorporating information in two ways:
information from digital elevation model (DEM) and from
VHR orthoimage. The two pictures are spatially registered.
Using this information, we obtain a novel approach to feature
extraction and classification that is described in the following.
The proposed method is composed of three main steps, as
described on Fig. 1:
Fig. 1.
General flowchart: we address the two last steps
1) Building footprint extraction (or roof segmentation). We
do not develop this step in this paper. It can be performed
using published methods using both the DEM and the
VHR data. Some techniques are mainly based on edge
and line primitives detection from an optical image [1]
978-1-4244-2808-3/08/$25.00 ©2008 IEEE
II. FEATURE EXTRACTION
The available data sets include a very high resolution
panchromatic image (airborne data, 25cm resolution) and a
digital elevation model (airborne acquisition, 1m resolution in
the three directions). These two modalities provide complementary information. We need to define features that allow to
separate flat roofs and gable ones.
A. Feature extracted from the DEM
Digital elevation model provides a description of the height
of the different pixels. In our study, the height difference
between the roof outline height and the ridge height is a
meaningful feature. This feature is a key in our pattern
recognition since higher values are expected for gable roofs:
gable roofs have a ridgeline that both roof surfaces meet and
corresponding pixels have a considerable height difference
when compared to the border pixels.
In order to calculate this height difference, we first need
to extract an accurate roof outline and perform a ridge line
detection. It is possible thanks to creation of one mask called
ψ(x, y) presented in Fig. 2 (a). This ideal mask is the binary
building footprint. It is defined as follows:
1 if x,y ∈ Roof
ψ(x, y) =
0 otherwise
The roof outline ψc (x, y) is trivially extracted from this
mask using a standard edge detector. It is presented in Fig. 2
(b). It is applied to the DEM leading to Hc (x,y) containing κ1
pixels:
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IGARSS 2008
Hc (x, y) = DEM (x, y) × ψc (x, y)
(1)
Once Hc (x,y) has been determined, the estimation of average roof outline height, denoted by Γ, can be derived as
follows:
Γ=
1
κ1
κ1
Hc (x, y)
(2)
n=1
κ2
1 Hs (x, y)
κ2 n=1
Υ=
(4)
With Υ and Γ, the difference Δ is computed:
Δ=Υ−Γ
(5)
The Δ computed for different roofs provided the expected
results: the magnitude of Δ as a matter of fact depends on
the roof-type. Regarding Section III, we notice that flat roofs
have lower Δ values than galbe ones. Hence, we propose Δ
as one roof feature capable of classifying different roof-types
using a simple threshold.
B. Feature extracted from the orthoimage
(a)
(b)
(c)
(d)
VHR orthoimage is a photographic map Fig. 3. In our study,
this image provides a representation of a two-dimensional
urban area. This kind of information is functional in order
to be able to evaluate or analyze building roofs. Searching
roof features, we take advantage again of the aspect of the
ridgeline: it usually appears with a strong grey level contrast
on the picture. This is due either to a change of illumination
on the different sides of the roof (hence inducing a shadow),
or to the use a metallic corner on top of the tiles.
Fig. 2. (a) Binary mask ψ(x, y) (b) Binary mask ψc (x, y) (c) DEM(x,y)
image (d) Binary mask ψs (x, y)
In a similar way, the extraction of the average ridgeline
height requires a mask to achieve a good ridgeline detection
in the DEM. To perform this detection, we consider roofs as
convex polygons. Consequently, the standard skeleton and the
medial axis are the same in the interior of the roof. Therefore,
the ridgelines of the roof can be extracted using one mask
denoted ψs (x, y) which is the roof skeleton. Note that this
feature does not depend on the actual shaoe of the roof. We
simply assume that for gable roofs, th ecentral part will be
significantly higher that the borders of the roof. This holds
even in the case of non perfectly symmetrical roofs. The
corresponding mask ψs (x, y) is shown in Fig. 2.
The application of ψs (x, y) to the DEM image leads to
Hs (x,y), with κ2 pixels:
Hs (x, y) = DEM (x, y)ψs (x, y)
(3)
Using these data points of Hs (x,y), the average ridge height
Υ can be estimated:
Fig. 3.
VHR orthoimage
One example of the contrast existing between different roof
sides is presented in Fig 4(a). This relation between the roof
gradient and the perception of contrast is explained by the grey
level gradient:
∂orto(x, y) ∂orto(x, y)
i+
j
∂x
∂y
orto(x, y) =
(6)
This is a vector with two components. The corresponding
norm is computed as:
orto(x, y) =
(
∂orto(x, y) 2
∂orto(x, y) 2
) +(
) (7)
∂x
∂y
The result of orto(x, y) is shown in Fig 4(c). We
can observe that different elements are detected besides the
ridgeline. Some details are visible on the top of the roof, such
as chimneys, dormers or structures to assist in the maintenance
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of the building . The presence of these elements is undesirable
in our feature extraction since these elements disrupt the
gradient image. Unfortunately, they are usually close to medial
axis in the case of flat roofs.
This problem gives way to one possible error in the pattern
classification if this contrast feature is used alone. Therefore,
we recommend filtering the roof image before proceeding to
the contrast feature extraction. This filter is made by one
standard morphological opening, whoch is defined as:
ortof (x, y) = orto(x, y) ◦ se = (orto(x, y) se) ⊕ se (8)
In the context of morphological transformations, the opening operation on a data set is classically defined as the
succession of an erosion and a dilation.
(a)
Θ=
κ3
1 Gs (x, y)
κ3 n=1
(10)
Theoretically, in the case of a gable roof, Θ should ahave
a larger magnitude than for a flat roof. However, results
presented in Section III do not perfectly corroborate this
expected discrimination.
III. CLASSIFICATION
The classification step aims at separating the two classes of
interest (gable roofs and flat roofs, respectively) using the two
extracted features (Δ and Θ). In order to ascertain if these
two features allow distinguishing roof-types, it is necessary
to make several observations. In our case, we have made
sixty different observations that have given rise to a couple
of features-specific vectors.
To interpret the vectors information, the gathered data are
represented using a scatter-plot of the feature values. Thanks
to this plot, we can determine graphically how to divide the
plane into homogeneous regions where all objects belong to
the same class. The result of our scatter-plot is shown in Fig. 5.
(b)
(a)
(c)
Fig. 5.
(d)
Scatter-plot
Fig. 4. (a) VHR orthoimage (b) Binary mask ψc (x, y) (c) VHR orthoimage
filtre (d) orto(x, y)
Applying this morphological transformation, the ridgeline
contrast in the image is enhanced satisfactorily as we show
in Fig 4(b), which is the result of the Fig 4(a) filtered. If we
make a comparison between both images, it is easy to perceive
that all the small elements have been removed.
In order to select ridge pixels, we use the ridge mask
ψs (x, y). However, it is slightly modified using a morphological dilation (leading to the mask ψsd (x, y)) as the contrast of
interest is not always very accurately located in the center of
the building. Using ψsd (x, y), Gs (x, y) is created as follows:
Gs (x, y) = ortof (x, y)ψsd (x, y)
(9)
Where Gs (x, y) is a set of κ3 pixels which provide information about ortof (x, y) on the roof skeleton. Finally,
the average of Gs (x, y) is computed:
(b)
Fig. 6.
(c)
(a) Separation according to Θ (b) Separation according to Δ
Two different colour markers are used in order to distinguish
each roof-type. Blue crosses are used for gable roofs whereas
flat roofs are represented as red crosses. Δ values are plot
along the horizontal axis while the vertical axis corresponds to
Θ values. Looking at fig 5 we can remark that the separation
between classes is evident. However, the results obtained from
the orthoimage called Θ are not as we expected. Θ values from
flat roofs are lower in average than values measured on gable
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roofs. However, the standard deviation is too high compared
to this difference to obtain a simple classifier based on this
feature only (see Fig. 6(a)).
(a)
(b)
(c)
(d)
misunderstanding in the evaluation of the real values of the
roof outline edge.
Despite of these limitations, the experimental results show
the robustness of our classification method. One example of
this achievement is the application of this method to Fig. 3.
The obtained solution is shown in Fig. 8 where flat roofs are
green whereas triangular roofs are represented in red.
Fig. 8.
Fig. 7. (a) VHR orthoimage (b) orto(x, y) (c) VHR orthoimage filtre
(d) ortof (x, y)
On the contrary, Δ values enable a very good separation
between the two patterns. Therefore, we can establish a good
differentiation. This separation is illustrated in Fig. 6(b).
Thanks to the effectiveness of Δ, we decided to extrapolate
the concept of this feature to make our classification more
general. The main reason is the existence of several triangular
roof types. Thus, our study does not have to be only focused
on gable roofs but used to separate all triangular roofs from
flat ones. Therefore, a parallel study of the basic DEM features
provided satisfactory results on different triangular roofs such
as hipped, saltbox, gambrel and pyramidal roofs. However,
we found several Δ limitations. For example, one important
limitation was found in hipped roofs because of the irregular
geometry of their surfaces. The problem is that ridgelines are
slightly out of the roof medial axis and that causes that the
highest part of the roof is not situated on the skeleton. Thus,
the height difference obtained from Δ is not exactly between
roof outline height and ridge height. Being not exactly could
not important in roofs with a hard slope on the roof surfaces. In
that case, although the height difference is not the real one, it
is enough performing to make a good classification. However,
the slight movement in ridgelines is a real problem for hipped
roofs with a low pitch. Being the height difference so small,
the triangular roof could be discriminated as a flat roof.
The second limitation comes from the existence of dormer
windows that protrude in many triangular roofs. These common features are aligned along the roof surface, normally
following the roof outline edge. They can be a problem since
dormers are represented in DEM images as pixels with a
high height value. In fact, those values could give rise to a
Results of Classification
IV. C ONCLUSION
Starting from two different data such as DEM images and
orthoimages, this paper has presented two skeleton features for
a satisfactory recognition and classification of roof-types. The
idea of two input data led us to develop a distinction between
roof-types that takes into account two roof features. However,
experimental results have shown that our classification can
be approached by using just one feature, Δ from DEM data.
This feature effectiveness achieves the purpose of this study
and moreover, it makes possible a new classification replacing
the pattern gable roof for all triangular roofs. Therefore, our
classification can be more general and then more effective to
perform a flat roofs isolation. It must also be noticed that
this generalisation for triangular roofs is also limited as we
mentioned in Section III.
R EFERENCES
[1] C. Jung and R. Schramm, “Rectangle detection based on a windowed
hough transform,” XVII Brazilian Symposium on Computer Graphics and
Image Processing (SIBGRAPI04), 2004.
[2] W. L. Z. LIU and J. WANG, “Building extraction from high resolution
imagery based on multi-scale object oriented classification and probabilistic hough transform,” Geoscience and Remote Sensing Symposium,
2005. IGARSS ’05. Proceedings. 2005 IEEE International, vol. 4, pp.
2250–2253, 2005.
[3] Z. Z. Yanfeng Wei and J. Song, “Urban building extraction from highresolution satellite panchromatic image using clustering and edge detection,” Geoscience and Remote Sensing Symposium, 2004. IGARSS ’04.
Proceedings. 2004 IEEE International, vol. 3, pp. 2008–2010, 2004.
[4] J. Chanussot, J. A. Benediktsson, and M. Fauvel, “Classification of remote
sensing images from urban areas using a fuzzy possibilistic model,” IEEE
Geoscience and Remote Sensing Letters, vol. 3, no. 1, pp. 40–44, 2006.
[5] M. Ortner, X. Descombes, and J. Zerubia, “Building outline extraction
from digital elevation models using marked point processes,” International Journal of Computer Vision, vol. 72, no. 2, pp. 107–132, 2007.
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