ilding detection 
ol. The feature 
features with a 
These training 
mount of image 
or each object 
> representative 
arability of the 
ently, SEaTH 
um separability 
Here, choosing 
es in the image 
that result in 
nction between 
' determined as 
been presented 
Case study 
e identified as 
ariable). 
[ON 
his paper as a 
igh resolution 
core of this 
[n this section, 
haracteristic of 
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ther classes in 
ires together in 
'eneralize this 
| the threshold 
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> and even any 
om making no 
1 seems to will 
nodel to other 
Parameter for 
according to 
| stages, giving 
omatic process 
rocess. 
to extract all 
and variable 
features together, it could not be accomplished as shown in the 
below image (figure 6). 
    
Figure 6. Resulted image 
Extraction of over 8096 buildings shown in the resulted image 
indicates successful applicability of algorithm presented in this 
research. Though, as mentioned, it was not successful in some 
cases, e.g. detecting buildings with light roofs (A). Since 
threshold of spectral features (Ratio B2 & Ratio B4) are 
arranged for detection of dark roofs and gable roof, this 
conclusion seems reasonable. This is because of failing analysis 
tool in extracting suitable features which result in optimal 
separation of building class with light roof from other classes. 
Also, in some cases, roof was not totally included in building 
class (especially in relation with middle line of buildings with 
gable roof). In these cases, objects with total vicinity with 
building class were regarded as building (Post-processing 
stage). 
As shown in the above image, some objects are classified in 
building class by mistake. In these cases, the intended object 
includes part of building and part of the adjacent phenomenon 
(B) that algorithm allocates these objects to the building class 
entirely. In these cases, it is possible to consider a smaller scale 
parameter that leads to the production of smaller objects. Of 
course, quality of detecting other buildings was reduced upon 
such segmentation. In other cases where big objects were 
allocated to building class by mistake (for example, two dark 
rectangles on the bottom of the image (C)), these objects were 
omitted from building class using area feature. Also, in some 
cases, though image objects were small and building roof and 
adjacent phenomenon were not simultaneously covered by that 
object, it was inevitably included in building class (D) since all 
conditions were qualified 
4. CONCLUSION 
This paper tends to extract building using an object based image 
analysis approach. This method allows us to use neighborhood, 
contextual and geometrical features in addition to spectral 
features. It has been attempted to present a general algorithm for 
building extraction from different satellite image. In the first 
step, the image pixels from the image are grouped to form 
objects with the aid of multiresolution segmentation algorithm. 
Then the features that lead to robust building extraction will be 
determined. Features are divided into two classes in this 
algorithm: stable and variable. Stable features are derived from 
inherent characteristics of building phenomenon and they 
provide us the possibility to be implemented on different 
satellite images. Variable features, depending on the case, are 
extracted using a feature analysis tool (SEaTH). Implementing 
this algorithm on a part of Isfahan QuickBird imagery was 
successful in extracting over 8096 existing buildings. Though in 
some cases the presented algorithm was not successful, the 
results were generally promising and the authors intend to 
examine complete transferability of classification model to other 
data sets and determine how much threshold values and features 
require adjustment and also find a solution for obtaining 
optimized scale parameter in an automatic process in the 
subsequent researches. 
REFERENCES 
Aminipouri, M., Sliuzas, R. and Kuffer, M., 2009. Object- 
Oriented Analysis of Very High Resolution Orthophotos for 
Estimating the Populations of Slum Areas, Case of Dar-Es- 
Salaam, Tanzania, High-Resolution Earth Imaging for 
Geospatial Information, ISPRS Workshop, Hannover, Germany. 
Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. and 
Heynen, M., 2004. Multi-Resolution, Object-Oriented Fuzzy 
Analysis of Remote Sensing Data for GIS-Ready Information. 
ISPRS Journal of Photogrammetry and Remote Sensing, 58(3- 
4): 239-258. 
Blaschke, T., 2010. Object based image analysis for remote 
sensing. ISPRS Journal of Photogrammetry and Remote 
Sensing. Vol. 65 (2010) 2 16. 
Dutta, D. and Serker, N.H.M.K., 2005. Urban Building 
Inventory Development using Very High Resolution Remote 
Sensing Data for Urban Risk Analysis. International Journal of 
Geoinformatics, , 1(1). 
Hofmann, P. (2001) Detecting urban features from IKONOS 
data using an object-oriented approach. First Annual 
Conference of the Remote Sensing & Photogrammetry Society. 
Munich, Germany. 
Hofmann, P., Strobl, J., Blaschke, T. and Kux, H., 2008. 
Detecting Informal Settlements from QuickBird Data in Rio de 
Janeiro Using an Object-Based Approach. In: T. Blaschke, S. 
Lang and G.J. Hay (Editors), Object-Based Image Analysis. 
Springer, Berlin Heidelberg, pp. 531-553 
Nussbaum, S., and Menz, G., 2008. Object-Based Image 
Analysis and Treaty Verification. Springer Science+Business 
Media B.V. 
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