Journal of Geographic Information System, 2011, 3, 217-224
doi:10.4236/jgis.2011.33018 Published Online July 2011 (http://www.SciRP.org/journal/jgis)
Copyright © 2011 SciRes. JGIS
Detecting Slums from SPOT Data in Casablanca Morocco
Using an Object Based Approach
Hassan Rhinane1*, Atika Hilali1, Aziza Berrada1, Mustapha Hakdaoui2
1Faculté des S ciences Aïn Chock , Université Hassan II-Casablanca, Casablanca, Maroc
2Faculté des Sciences Ben M’sik, Université Hassan II-Mohammedia-Casablanca, Casablanca, Maroc
E-mail: firstname.lastname@example.org, email@example.com, berrada_a zi firstname.lastname@example.org , hakdaoui@gm ail.com
Received April 2, 2011; revised May 23, 2011; accepted June 5, 2011
Casablanca, Morocco’s economic capital continues today to fight against the proliferation of informal settle-
ments affecting its urban fabric illustrated especially by the slums. Actually Casablanca represents 25% of
the total slums of Morocco . These are the habitats of all deprived of healthy sanitary conditions and
judged precarious from the perspective humanitarian and below the acceptable. The majority of the inhabi-
tants of these slums are from the rural exodus with insufficient income to meet the basic needs of daily life.
Faced with this situation and to eradicate these habitats, the Moroccan government has launched since 2004
an entire program to create cities without slums (C.W.S.) to resettle or relocate families. Indeed the process
control and monitoring of this program requires first identifying and detecting spatial habitats. To achieve
these tasks, conventional methods such as information gathering, mapping, use of databases and statistics
often have shown their limits and are sometimes outdated. It is within this framework and that of the great
German Morocco project “Urban agriculture as an integrative factor of development that fits our project de-
tection of slums in Casablanca. The use of satellite imagery, particularly the HSR, has the advantage of
providing the physical coverage of urban land but it raises the difficulty of choosing the appropriate method
to apply.This paper is actually to develop new approaches based mainly on object-oriented classification of
high spatial resolution satellite images for the detection of slums.This approach has been developed for
mapping the urban land through by integration of several types of information (spectral, spatial, contextual ...)
(Hofmann, P ., 2001, Herold et al. 2002b; Van Der Sande et al., 2003, Benz et al., 2004, Nobrega et al.,
2006). In order to refine the result of classification, we applied mathematical morphology and in particular
the closing filter. The data from this classification (binary image), which then will be used in a spatial data-
Keywords: Slums, Urban Remote Sensing, SPOT 5, Object Based Image Analysis, ArcGis
The study by the World Bank  showed that Casa-
blanca has the largest population of slums with approxi-
mately 45,000 households located in 270 nuclei, or a
quarter of the total national population of urban slums.
This type of habitat is populated by the most impove-
rished social strata, not complying with the Building
Standards (illegal or non-regulatory), with a low or ab-
sent infrastructure and no security measure . Often
these habitats are covered with recycled materials (sheets,
plastics, wood ...) and organized as clusters of strong
condensation of several informal settlements. Some
neighbourhoods such as Hay Mohammadi, Ben M’sik,
Sidi Moumen has always housed the largest concentra-
tions of slums and even wealth y neighbourho ods as Anfa,
Ain Diab, Racine, California have their own islands of
poverty so that it becomes excessive to conclude to a real
structure of spatial segregation . View the restriction
of traditional methods employed (field visits/observation)
and the unknown dynamic of the metropolitan population
, the Moroccan government adopted in January 2005,
a new innovative approach to monitor and map the slums
through remote sensing (aerial photographs, satellite im-
agery ...) in some pilot cities.
Certainly, satellite imagery has shown several ad-
H. RHINANE ET AL.
vances in extraction of different urban entities providing
timely and accurate responses to the occupation of urban
land. However, very few studies have focused on map-
ping slums in urban areas [2,5-10] but their application
in urban North Africa today remains marginal or even
The objective of this work is to extract, identify and
map these slums in the city of Casablanca in 2004
through an object-oriented classification of a SPOT-5 at
2.5 m spatial resolution.
2. Material and Methods
2.1. Location of the Study
Morocco’s economic capital and one of the largest cities
in the continent, Casablanca is the largest city in Mo-
rocco with more than 3 million inhabitants with a total
area of 1,140.54 km2  (Figure 1). Casablanca is
located on the Atlantic coast about 100 km south of the
administrative capital (Rabat). The Wilaya of Greater
Casablanca includes two prefectures (Mohammedia &
Casablanca) and two Provinces (Nouaceur or Nouasser
and Mediouna) for a total of 17 municipalities, 10 urban
and seven rural communities.
2.2. Available Data and Preprocessing
Satellite images become now an essential tool for plan-
ning and land development due to the frequency of shots
and the spatial resolution more and more efficient. The
launch of satellites with high spatial resolution (HSR),
such as SPOT-5 (2002) with 10 m spatial resolution
multi-spectral mode and 5 m spatial resolution in pan-
chromatic mode, allowed the extraction of different ur-
ban entities. The main data used in this work are:
Image of SPOT-5 Casablanca taken March 16, 2004,
georeferenced, orthorectified and merged with 2.5 m
spatial resolution. This picture was taken during a
sunny month which helped the contrast between dif-
ferent urban entities. To improve the visual quality of
this picture we have carried image enhancement and
filtering operations (Figure 2).
Cards Master of Urban Planning of the Wilaya of
Greater Casablanca provided by the Urban Agency of
Casablanca used to validate.
Topographic map of the city of Casablanca, (NI-29-
sheet XI-3B) wide 1/50000 used for tracking in the
field and for areas difficult to access. The images of
Google Earth Server Map and Goog le Map have been
The satellite data processing was performed on Erdas
Imagine then Envi Zoom. The classification results were
vectorized and exported to ArcGIS.
2.3. Methodological Approach
The study of the urban fabric has always interested
Figure 1. Geographical location of the city of Casablanca.
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.219
04 7002 350Me te rs
Figure 2. (a) merged Image SPOT-5 of the city of Casablanca; (b) Overview of slums on image SOPT-5 of Casablanca.
several researchers in management and urban planning.
The use of satellite imagery has the advantage of pro-
viding the physical coverage of urban land but it raises
the difficulty of choosing the appropriate method to
apply. Several pattern recognition algorithms are used in
urban areas on images HSR: the urban morphology
[11-15], INDVI , the image thresholding , neural
networks [18,19], semi-automatic procedure , Digital
Elevation Model , yet the object - oriented approach
[22,23], the index of landscape , clustering and
geographic information system [24,25].
Choosing one method over another is not easy and
depends on data availability and purpose of the study. In
the context of detecting the slums, some studies have
used aerial photographs , others a semi automatic [20 ]
or supervised classification by maximum likelihood .
Also, urban space, by its very heterogeneous nature,
becomes more complex to study by the presence in soil
of many objects different by their shape, direction, size,
material depending on the type of habitat  and this in
addition to the constraints inherent to the pixel size (2.5
m) . The combination of the texture will certainly
improve the quality of classification [26-28] and will
make easier the identification of the slums among other
objects with similarities and which can lead to confusion
in interpretation (built dense, unhealthy habitat, roof...).
Since decades, the approach classification “object -
oriented” has been developed for mapping the urban land
through the integration of several types of information
(spectral, spatial, contextual ...) [6,23,29-31] hence its
adoption for this project. Figure 3 illustrates the metho-
dology adopted in this work to map the slums of Casa-
2.4. Object-Oriented Classification
The object-oriented classification in ENVI is an iterative
process carried out in two stages:
Segmentation: sequence of several steps of segment-
Acquisition of image SPOT-5
(16 mars 2004)
Multi-r es olut ion Se
Fusion of image (panchromatic)
Spatial resolution 2.5m
of spatial analysis
Map of slums
Figure 3. Methodological flowchart.
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.
ing the image into regions.
It consists to subdivide the image in the visible range, in
homogeneous regions, taking into account the spectral
parameters (mean, standard deviation index calculation),
space (size, shape) and contextual (spatial relationships
between regions) [32-35]. During this process, segments
are created by fusion of adjacent pixels forming sub
objects that will be gradually merged with their neigh-
bors to create objects of sizes larger and larger with
similar forms well distributed in the urban space accord-
ing to a scale parameter (or criterion of heterogeneity)
defined by the user. The objects thus created are linked
together by a hierarchical network representing the rea-
lity on the field [28-36] (Figure 4). This parameter, also
called “weight” determines the segmentation process and
therefore the size of the segmented objects. The higher
this parameter is, the higher the items are of larger size.
These parameters were set at 30 for level 1 and 93.3 for
2.4.2. Classification of the Image SPOT-5
After segmentation, the second step is to group objects
and assign them a p robab ility or d egree of belon ging to a
given class from the robust rules of recognition of the
regions. Several knowledge rules can be combined to
define the classification rule of a given object, corre-
sponding to the method of “nearest neighbors”, or the
method of “membership functions (fuzzy logic) or a
mixture of two .
Supervised classification in ENVI Feature Extraction
is an iterative process from the “nearest neighbor”
algorithm based on K-nearest neighbor. The advantage
of the approach “nearest neighbor” is that classes are
Figure 4. Principle of multi-resolution segmentation modi-
fied of .
defined based on a variety of attributes spatial, spectral
and textural and is dependent on the choice of training
The choice of training sites was an important step in
the classification process adopted. These training sites
were chosen carefully for they are as representative as
possible of the class slums. However, due to the strong
heterogeneity of the urban environment and the com-
plexity of the obj ects to be extracted, the result of object-
oriented classification of SPOT-5 image shows the pre-
sence of bad pixels assigned to predefined themes giving
a salt-effect pepper.
Certainly, the best classification results are closely
related to the choice of training sites and in order to
define the different thematic classes we performed an
unsupervised classification based primarily on spectral
signatures. Roughly, two classes are distinct urban image
SPOT-5 (Frames, vegetation (dense and grass)). Thus a
wide range, the most representative samples were
collected for major urban classes (vegetation, built and
slums). The result of this classification is a binary image
2.4.3. Vali da ti on of th e Cl assification
The evaluation of a classification is a complex concept
that includes the reference to several criteria that can
occur in several stages . The main idea is to deter-
mine the accuracy of this classification by comparing the
results with data provided from the reality in the field.
These realities comes from Master Plan (Department of
Urban Planning of the Wilaya of Greater Casablanca 
provided by the urban agency in Casablanca and physical
In general, the classification evaluation is done by
calculating two indices from the confusion matrix: over-
all accuracy and Kappa index. The Kappa index indicates
how to classify the data agree with reference data [41,42]
Given that the main theme of this study focuses on one
topic or class slums, the evaluation of this classification
will be calculated manually empirically using the ge-
ographic information system (ArcGIS) and this by
calculating commission and omissions errors on this
3. Results and Discussions
The choice of training sites was an important step in the
classification process adopted. These training sites were
chosen carefully for they are as representative as possible
of the class slums. However, due to the strong heteroge-
neity of the urban environment and the complexity of the
objects to be extracted, the result of object-oriented clas-
sification of SPOT-5 image shows the presence of bad
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.221
pixels assigned to predefined themes giving a salt-effect
pepper. The result of the classification (binary image) is
then exported to ArcGIS, mapping of slums in the city o f
Casablanca is achieved.
In order to remove misclassified pixels and to refine
the result of classification, we applied mathematical mor-
phology and in particular the closing (succession of dila-
tion and erosion). The tests showed that the window of
the structuring element for better results is that of 9 * 9
After the classification and the passage of the filter
(closing), evaluation of results was done according to
quantitative (number of slums correctly present in the
SDAU, 2004; errors of commission and omission) and
qualitative methods (fo rm, difference between slums and
informal settlement...). The overlay map slums (SDAU)
in 2004, provided by the urban agency in Casablanca, on
the map after the object-oriented classification of the
SPOT-5 shows that 64 of 75 slums have been properly
classified and 29 items were incorrectly assigned to this
class. Thus, the commission error is 29 * 100/93 = 31%
and omission error is 11 * 100/64 = 17%, precision user
(Pu) is 64 * 100/93 = 69% and the accuracy of producer
(Pi) is 64 * 100/75 = 85%. We assume that the precision
obtained through the classification of object-oriented
image SPOT-5 is quite high.
Knowing the heterogeneity and complexity of the
spectral coverage of urban land, and for the spatial
resolution of the image used, some objects appear to
have similarities (show and textural) with slums which
implies their assignment to this class. Analysis of urban
objects committed to the class slums shows some visual
similarity between them because of their shape and their
spectral signature (Figures 6 and 7) these objects are
either slums, densely built or in some cases bare soil.
On slums that were omitted from the classification are
primarily small sizes averaging 3800 m2 or 240 pixels,
Figure 5. Image SPOT-5 classified.
Figure 6. Section of the SPOT-5 image of the city of Casa-
blanca showing habitats unhealthy (white) assigned to the
class of slum.
Figure 7. Section of the SPOT-5 in the Casablanca slums
showing (red) unclassified.
these clusters are isolated rather than clustered buildings.
Also, these slums have different spectral responses from
those of all slums and who were chosen as training site
Through this study, we demonstrated the importance of
using satellite images with high spatial resolution
SPOT-5 in particular the id entification and quan tification
of these slums in the city of Casablanca. We also showed
that object-oriented classification approach has proven
remarkably effective for the extraction of slums (85%).
This result is satisfactory and fulfills ou r stated obj ectiv e.
It also confirms the relevance of the image to SPOT5 2.5
m spatial resolution to discriminate the class slums pre-
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.
Figure 8. Sections of the SPOT-5 wi th slums unc lassifie d.
ssent in a heterogeneous environment such as urban
Even if the result obtained using the “feature extrac-
tion” ENVI Zoom gives reliable results, but it never-
theless remains limited. This limitation is due to the fact
that some urban classes with a high spectral and textural
similarity may be confused and will diminish the quality
of the classification where the complexity of extracting
the urban class.
This research may provide a basis for more advanced
work on the implementation of a map of urban settlement
where we can distinguish between different urban en-
tities. Thus, the map after this classification will be used
as a basis for establishing a spatial vector database of the
slums of the city of Casablanca. Another approach based
on an analysis of the diachronic evolution of the class of
slums can also be the subject of new research or even an
estimate of the urban slum .To do so would require
several images taken at different times as this will also
identify the slums but also to assess the rate of pro-
gression and use of satellite images of higher resolution
will be an asset (eg IKONOS 1m spatial resolution).
 Banque mondial, “Rapport de L’Analyse D’lmpact Social
et Sur la Pauvreté,” 2006, p. 62.
 K. Busgeeth, J. Whisken and A. Brits, “Potential Appli-
cation of Remote Sensing in Monitoring Informal Settle-
ments in Developing Countries Where Complimentary
Data Does Not Exist,” Planning Africa Conference—
Shaping the Future, Johannesburg, 4-16 April 2008, pp.
 M. Laoudi, “Casablanca a travers ses petits entrepreneurs
de lapauvrté. Apprécu sur les micro-activités marchande s
de rue dans une métropole maghrébine. Série thèse et
mémoire N 8. Université Hassan II Aîn Chock, Faculté
des lettres et sciences humaines Casablanca, 2001, p. 319,
 S. Gadal, “Urbanisation et Dynamiques de Peuplements:
Casablanca, 1994-2002, UMR 63 IRD-UVSQ,” Ver-
sailles Saint-Quentin-en-Yvelines University, Ver- sailles,
 S. Mason, C. Fraser, “Image Sources For Informal
Settlement Management,” Photogrammetric Record, Vol.
16, No. 92, 1998, pp. 313-330.
 P. Hofmann, “Detecting Informal Settlements from IK-
ONOS Image Data Using Methods of Object Oriented
Image Analysis: An Example from Cape Town, South
Africa,” In: C. Jrgens, Ed., Remote Sensing of Urban Ar-
eas, Regensburger Geographische Schriften, Regensburg,
2001, pp. 107-118.
 P. Hurskainen and P. Pellikka, “Change Detection Of
Informal Settlements Using Multi-Temporal Aerial Pho-
tographs—The Case of Voi, SE-Kenya,” Proceedings of
the 5th AARSE Conference, 18-21 October 2004, Nairobi,
 T. Lemma, “A Comparison of Methodologies for Moni-
toring Slum Conditions within Millennium Development
Goals, the Case Study of Addis ababa, Ethiopia, Thesis
for Master,” International Institute for Geo-information
Scinces and Eart h Observation, Ensch de, 2005.
 P. Hofmann, J. Strobl, T. Blaschke and H. Kux, “Detect-
ing Informal Settlements from Quickbird Data in Rio de
Janeiro Using an Object Based Approach,” Accessed 25
 G. Skupinski, D. BinhTran and C. Weber, (2009).
“Lesimages Satellites Spot Multi-Dates et la Métrique
Spatiale dans L’etude du Changement Urbain Et
Suburbain—Le Cas de la Basse Vallée de la Bruche
(Bas-Rhin, France),” Cybergeo: European Journal of
Geography, Systèmes, Modélisation, Géostatistiques,
article 439, mis en ligne le 12 mars 2009, modifié le 03
 W. K. Pratt, “Digital Image Processing,” Willey in-
ter-sciences, New York, 1978, p. 743. M. Robin, “La
télédétection,” Nathan, Paris, 1995, p. 318
 Y. Baudot, “Télédétection Aérospatiale et Analyse
Géographique de la Population des Villes dans les Pays
en Développement,” Thèse de doctorat, Université Catho-
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.223
lique de Louvain, Louvain-la-Neuve, 1994.
 P. Terrettaz “Délimitation des agglomérations et
segmentation urbaine à l’aide d’images satellites Spot
HRV,” Thèse de Doctorat, Université de Fribourg, Fri-
 J. A. Benediktsson, M. Pe saresi and K. Amason, “Classi-
fication and Feature Extraction for Remote Sensing Im-
ages From Urban Areas Based on Morphological Trans-
formations,” IEEE Transactions on Geoscience and Re-
mote Sensing, Vol. 41, No. 9, 2003, pp. 1940-1949.
 D. Sheeren, S. Lefèvre and J. Weber, “La Morpho-
Logiemathématique Binaire Pour L’extraction Auto-
matique des Bâtiments dans les Images THRS,” Revue
Internationale de Géomatique, Vol. 17, No. 3-4, 2007, pp.
 J. G. Masek, F. E. Lindsay and S. N. Goward, “Dynamics
of Urban Growth in the Washington D.C. Metropolitan
Area, 1973-1996, from Landsat Observations,” Interna-
tional Journal of Remote Sensing, Vol. 21, No. 18, 2000,
pp. 3473-3486. doi:10.1080/014311600750037507
 T. Fung, “An Assessment of TM Imagery for Land Cover
Change Detection,” IEEE Transactions on Geoscience
and Remote Sensing, Vol. 28, No. 4, 1990, pp. 681-684.
 X. Chen, X. Li and J. Ma, “Urban Change Detection
Based on Self-Organizing Feature Map Neural Network,”
Proceedings of the IEEE International Symposium on
Geoscience and Remote Sensing, Vol. 5, 20-24 Septem-
ber 2004, pp. 3428-3431.
 P. Gamba, F. Dell’Acqua, “Increased Accuracy Multi-
band Urban Classification Using a Neuro-Fuzzy Classi-
fier,” International Journal of Remote Sensing, Vol. 24,
No. 4, 2003, pp. 827-834.
 M. Stasolla and P. Gamba, “Exploiting Spatial Patterns
For Informal Settlement Detection Inarid Environments
Using Optical Spaceborne Data,” International Archives
of Photogrammetry, Remote Sensing and Spatial Infor-
mation Sciences, Vol. 36, 2007, pp. .
 C. H. William, F. C. Williamand and K. Gyimah, “High-
Resolution Lidar-Based Landslide Hazard Mapping and
Modeling, UCSF Parnassus Campus, San Francisco,
USA,” Bulletin of Engineering Geology and the Envi-
ronment, Vol. 68, No. 2, 2009, pp. 263-276.
 V. Walter, “Object-Based Classification of Remote
Sensing Data for Change Detection,” ISPRS Journal of
Photogrammetry and Remote Sensing, Vol. 58, No. 3-4,
2004, pp. 225-238.
 U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder,
and M. Heynen, “Multi-Resolution, Object-Oriented
Fuzzy Analysis of Remote Sensing Data for GIS-ready
information,” ISPRS Journal of Photogrammetry and
Remote Sensing, Vol. 58, No. 3-4, January 2004, pp.
 G. Sartori, G. Nembrini and F. Stauffer, “Monitoring of
Urban Growth of Informal Settlements and Population
Estimation from Aerial Photography and Satellite Imag-
ing,” 2002. Occasional paper-thirstycitiesinwar.com.
 J. Abbott, “The Use of GIS in Informal Settlement Up-
grading: Its Role and Impact on The Community and on
Local Government,” Habitat International, Vol. 27, No.
4, 2003, pp. 575-593.
 A. Michel and B. Lortic, “Typologies Urbaines Et
Télédétection Satellitaire: La Notion de Zones en Milieu
Urbain,” Espace Géographique (FRA), Vol. 21, No. 2,
1992, pp. 167-178.
 T. Bauer and K. Steinccocher, “Per_Parcel Land Classi-
fication in Urban Areas Applying a Rule-Based Tech-
nique,” GeoBIT/GIS, Vol. 6, 2001, pp. 24-27.
 A. Puissant and C. Weber, “Démarche Orientée
‘Objets-Attributs’ et Classification D'images THRS,”
Revue Française de Photogrammétrie et De Télé-
détection, Vol. 173-174, 2004, pp. 123-134.
 M. Herold, M. Gardner, B. Hadley and D. Roberts, 2002b.
“The Spectral Dimension in Urban Land Cover Mapping
from High-Resolution Optical Remote Sensing Data,”
Proceedings of the 3rd Symposium on Remote Sensing of
Urban Areas, June 2002, Istanbul, on CD Rom.
 C. J. Van Der Sande, S. M. De Jong, A. P. J. De Roo, “A
Segmentation and Classification Approach of IKONOS-2
Imagery for Land-Cover Mapping to Assist Flood Risk
and Flood Damage Assessment,” International Journal of
Applied Earth Observation and Geoinformation, Vol. 4.
No. 3, 2003, pp. S217-S229.
 R. A. A. Nobrega, C. G. O’Hara, J. A. Quintanilha, “De-
tecting Roads in Informal Settlements Surrounding, Sa-
opaulo City by Using Object_Based Classification,”
Proceedings of the 1st International Conference on Ob-
ject- Based Image Analysis, Salzburg, 2006, pp. .
 M. Baatz and A. Schäpe, “Multiresolution Segmentation
—An Optimization Approach for High Quality Multi-
Scale Image Segmentation,” Angewandte geographische
Informationsverarbeitung XII: Beiträge zum
AGIT-Symposium, Salzburg, 2000, pp. 12-23.
 J. Schieve, “Segmentation of High-Resolution Remotely
Sensed Data—Concepts, Applications and Problems,”
Symposium on Geospatial Theory, Processings and Ap-
plications, Ottawa, 2002.
 T. Esch, A. Roth, S. Dech, “Robust Approach Towards
an Automated Detection of Built-up Areas from High
Resolution Radar Imagery,” Proceedings of the ISPRS
WG VII/1 “Human Settlements and Impact Analysis” 3rd
International Symposium Remote Sensing and Data Fu-
sion Over Urban Areas and the 5th International Sympo-
sium Remote Sensing of Urban Areas. Tempe, 14-16
 A. Karsenty, Antunes, B. A-F, Centeno, J-Silva. (2007)
“Classification Orientée Objet de la Perméabilité des Sols
en Zone Urbaine a L’aide D’imagerie Très Haute
Résolution et de Données Laser Scanner à Curitiba
(Brésil)” Anais XIII Simpósio Brasileiro de
Sensoriamento Re moto, Florianópolis, INPE, p. 565-572
 E. Freauman, E. Wolf, “Segmentation of Very High Spa-
Copyright © 2011 SciRes. JGIS
H. RHINANE ET AL.
Copyright © 2011 SciRes. JGIS
tial Resolution Satellite Images in Urban Areas for Seg-
ments-Based Classification,” Proceedings of the 3rd In-
ternational Symposium Remote Sensing and Data Fusion
Over Urban Areas, Tempe, 2005, pp. .
 L. Sparfel, F. Gourmelon and I. Le Berre, “Approch
Orienté-Objet de L’occupation des Sols en Zone Co-
tière,” Revue Télédétection, Vol. 8, No. 4, 2008, pp.
 G. Willhauck, “Comparison of Object Oriented Classifi-
cation Techniques and Standard Image Analysis for the
Use of Change Detection between SPOT Multispectral
Satellite Images and Aerial Photos,” International Ar-
chives of Photogrammetry and Remote Sensing, Vol. 33,
2000, pp. 214-221.
 R. Caloz and C. Collet, “Précis de Télédétection,” Traite-
ments numériques d’images de télédétection, Presses de
l’Université du Québec/AUPELF, Sainte Foy, Vol. 3,
 SDAU (2004) “Plan de développement stratégique et
Schéma Directeur d’Aménagement urbain de la Wilaya
du Grand Casablanca, Projet de diagnostic et Enjeux du
développeme n t , rapport N°3.
 R. G. Congalton “Eview of Assessing the Accuracy of
Classifications of Remotely Sensed Data,” Remote Sens-
ing of Environment, Vol. 37, 1991, pp. 35-46.
 R.-G. Conglaton and K. Green, “Assessing the Accuracu
of Remotely Sensed Data: Principles and Practices,”
Lewis Publishers, New York, 1999.
 M. Aminipouri, “Objected-Oriented Analysis of very
High Resolution Orthophotos for Estimating the Popula-
tion of Slum Areas,” Case of Dar-Es-Salam, Tanzania,