separate non-slum
jects. Hence the
he relative border
the NB2 and NBI
) image objects. It
ngth of an image
igned to a defined
ative border of an
ass is 1, the image
ative border is 0.5
of its border. The
ed in pixels. The
; used to separate
S.
shadow in built-up
1m areas
object level, it can
1owledge to obtain
ene or to aid in
1s are tends to be
se to river and
efore Distance to
vere also used to
Id photos
ining built-up was
1 formal was first
ts can be better
understood. Since the informal settlements tend to cluster
themselves, it can be differentiated easily from other formal
areas based on their area. Therefore ‘area’ feature was used to
identify the slums in eCognition environment. The false
positives 1.e., non-slum areas looking like slums were
eliminated by using adequate rules sequentially. The false
positives are happening because of non-visible slums
exemplified by planned but deteriorating inner cities (Turkstra,
2008). The false positives were reclassified into non-slum
areas, by using geometry of the object such as area, asymmetry,
shape index and also contextual parameters such as distance to
the object. Similarly some slum areas which were surrounded
by non-slum areas also included in non-slum areas. To rectify
this, relation to the border of the object and distance to
parameters were used. So that the slums which included in
non-slum areas were classified correctly Thus finally the image
was classified (figure 8) into slums and non-slums along with
other non- built-up areas.
N
0 250500 1.000 1,500 2.000
UNE am Meters
Figure 8. Classified Image of central part of Pune city
3.4 Accuracy assessment
The evaluation of a classification is a complex concept that
includes the reference to several criteria. The main idea is to
determine the accuracy of this classification by comparing the
results with data provided from the reality in the field. These
realities come from the slum survey carried out by city based
NGO’s and Pune Municipal Corporation’s environmental
status report(2010).
User Class \ Serncde | Formal HO | sum
Canfunion Mairie
Formal & 36 657
Informal B 40 46
uralssetied 8 48 5
Sum Es 124
Accuracy
PEN (3767302 03225308
ser 9635552
SIRE (346 (9635552
Overall Accuracy 0.9714296
KIA 0.5123278
de 5 opm [Eee]
Figure 9. Accuracy assessment
Through field survey and primary data collected from slum
dwellers, the slum map was created. The classification result
was compared with slum map prepared based on Slum Survey.
The overall accuracy is 87 % (Figure 9).
4. CONCLUSION
The issue of slums is very complex. Detecting slums might be
one of the most challenging tasks within urban remote sensing.
Though the present study demonstrated the advantage of VHR
data and OOA approach in detecting the slums, it required
local knowledge of existing slums and their characteristics.
Using thematic layers such as roads and water bodies saved the
time and reduced the complexity of rule set on extracting roads
and water bodies from the image and thus helped to
concentrate on detecting and discriminating slums from non-
slums. One major issue in this analysis is false positives.
Thorough understanding of study area is essential to develop
the rule set to diminish the false positives. Detailed field visit
is also essential to develop the rule set and can help to achieve
reasonable accuracy. But complete removal of false positives is
not possible in the inner/old city area because of its
complexity. The present study area is mainly covering the
indigenous city of Pune. So, complete clean-up was not
possible and not done in the present study.
Applying the same rule set to other scenes of quick bird data
was also tried and the results are promising. But the threshold
values for various rules such as brightness values, GLCM
values etc. have to be modified as per the scene characteristics.
Thus the present work may provide a basis for more advanced
research to generalise a rule set which can be applied to
various scenes of the same city and to various cities.
5. ACKNOWLEDGEMENTS
The author would like to thank Dr. Richard Sliuzas and
Dr. Norman Kerle of ITC-Faculty of Geoinformation Science
& Earth Observation, University of Twente, The Netherlands
for their continuous support and valuable inputs during this
work. Special thanks to Divyani Kohli and Deepti Durgi, (Ph.D
scholars, ITC) for assisting in learning the eCognition software
and sharing the Quickbird data. Sincere thanks to European
Commission for providing Post Doctoral Research Fellowship
to complete this work.
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