Automatic Fuzzy Object-based Analysis of VHSR Images for Urban 2

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Automatic fuzzy object-based analysis of VHSR images for urban objects extraction

Imane Sebari a,⇑, Dong-Chen He b

a Filière de Sciences Géomatiques et Ingénierie Topographique, IAV Hassan II, Rabat, Moroccob Centre d’applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, Sherbrooke, Québec, Canada

a r t i c l e i n f o

Article history:

Received 15 September 2012Received in revised form 5 February 2013

Accepted 5 February 2013

Available online 25 March 2013

Keywords:

Automatic object extraction

Object Based Image Analysis (OBIA)

Fuzzy rule base

VHSR satellite images

Urban areas

a b s t r a c t

We present an automatic approach for object extraction from very high spatial resolution (VHSR) satellite

images based on Object-Based Image Analysis (OBIA). The proposed solution requires no input data otherthan the studied image. Not input parameters are required. First, an automatic non-parametric coopera-

tive segmentation technique is applied to create object primitives. A fuzzy rule base is developed based

on the human knowledge used for image interpretation. The rules integrate spectral, textural, geometric

and contextual object proprieties. The classes of interest are: tree, lawn, bare soil and water for natural

classes; building, road, parking lot for man made classes. The fuzzy logic is integrated in our approach in

order to manage the complexity of the studied subject, to reason with imprecise knowledge and to give

information on the precision and certainty of the extracted objects. The proposed approach was applied

to extracts of Ikonos images of Sherbrooke city (Canada). An overall total extraction accuracy of 80% was

observed. The correctness rates obtained for building, road and parking lot classes are of 81%, 75% and

60%, respectively.

2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier

B.V. All rights reserved.

1. Introduction

Mapping impervious surface from remote sensing imagery is

significant to a range of issues especially to sustainable develop-

ment of urban areas (Demarchi et al., 2012; Gao et al., 2012;

Longley et al., 2005; Holland et al., 2006; Donnay et al., 2001). Very

high spatial resolution (VHSR) images and advanced image pro-

cessing algorithms both driven the technologic advance in remote

sensing of impervious surfaces (Xu, 2013). One of the emerging

trends in this field is OBIA, object-based image analysis (Weng,

2012). OBIA is considered as a powerful tool for classification and

analysis of VHSR images compared to the traditional per-pixel

classifiers (Blaschke, 2010; Navulur, 2007; Blaschke et al., 2000).

The advantage of OBIA is that it does not use individual pixels

but adjacent pixel groups that can be characterized by spectral,textural, geometric and contextual information. Taking this infor-

mation into account through the object-based approach allow

obtaining enhanced results (Campbell, 2007).

Object based image analysis has been defined as a new disci-

pline at the first international conference on Object-Based Image

Analysis:

‘‘Object-Based Image Analysis (OBIA) is a sub-discipline of

GIScience devoted to partitioning remote sensing (RS) imagery into

meaningful image-objects, and assessing their characteristicsthrough spatial, spectral and temporal scale.’’ (Hay and Castilla,

2006).

The OBIA, also called object oriented image analysis, aims to

replicate and/or to surpass the human interpretation of images

automatically or semi-automatically (Hay and Castilla, 2006).

Two main stages can form the OBIA process: (1) creation of image

objects and (2) classification of image objects. Usually, the first

step is performed through a segmentation technique (Lang and

Blaschke, 2006; Jensen, 2005). This step is a crucial since it pro-

vides the basic units (image objects) on which later process will

be applied. Therefore, the success of OBIA approach is related to

segmentation quality. The second stage, classification, tries to cre-

ate ‘real’ objects from ‘image’ objects. The classification method is

chosen with relation to the desired goal, to the studied image, and

also to its ability to integrate ancillary information. Several meth-

ods can be used at the two stages of the OBIA approach. The chosen

algorithms strongly influence the final results (Lu and Weng, 2007;

Caloz and Pointet, 2003).

The first known reference that used the object-based approach

was Kettig and Landgrebe (1976). They proposed a classification

approach of multispectral images by extracting and classifying

homogenous objects. Their approach consists first in subdividing

the image in spectrally homogenous pixel groups. These groups

are then classified through supervised technique (maximum likeli-

hood). They applied their approach to aerial and satellite images

0924-2716/$ - see front matter 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.isprsjprs.2013.02.006

⇑ Corresponding author. Tel.: +212 650632611; fax: +212 37680180.

E-mail addresses: [email protected], [email protected] (I. Sebari).

ISPRS Journal of Photogrammetry and Remote Sensing 79 (2013) 171–184

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(Landsat). This method is still used under the name of ECHOs

(Extraction and Classification of Homogeneous Objects). It is avail-

able in an open-source environment – Multispec (Biehl and Land-

grebe, 2002). Lee et al. (2003) applied it to extract shapes and

positions of building from Ikonos image. The ECHO approach has

been used by Jiménez et al. (2005) under a non-supervised version

(UnECHO) to extract homogenous regions from hyperspectral

images.The use of OBIA became more widespread with the advent in

2000 of eCognition, the first commercially available, object based

image analysis software (Blaschke, 2010). It is today known as

Definiens. The object image creation step is conducted by a multi-

resolution segmentation based on the Fractal Net Evolution Ap-

proach (Baatz and Schäpe, 2000). The segmentation algorithm is

a bottom-up region-growing technique. The growing decision is

based on local homogeneity criteria describing the similarity of

adjacent image objects in terms of size, distance, texture, spectral

similarity and form (Baatz and Schäpe, 2000). User-defined thresh-

olds are interactively used to decide whether objects are merged

into larger objects or not. For image object classification, two

methods are proposed: the nearest neighbor and fuzzy rules base

(Benz et al., 2004). Several attributes (spectral, geometric and con-

textual) can be integrated in the classification process. Significant

studies have used this software in different applications (forestry,

urban, agriculture, coastal zone, etc.). Regarding VHSR satellite

images of the urban area, they attempted to interpret either the

whole image (Myint et al., 2011; Kux and Araújo, 2008; Marchesi

et al., 2006; Caprioli and Tarantino, 2003; Mittelberg, 2002; Kress-

ler et al., 2001; Meinel et al., 2001), or to extract specific objects

such as buildings (Hofmann, 2001a), roads (Repaka et al., 2004;

Nobrega et al., 2008), and private gardens (Mathieu et al., 2007)

or informal settlements (Hofmann et al., 2008). Recently, other im-

age analysis software has developed OBIA modules like Feature

Analyst (Tsai et al., 2011) or ENVI Feature Extraction (Hu and

Weng, 2011).

Compared to the pixel-based approach, the extracted objects by

an OBIA approach are more homogeneous than by pixels based ap-proach and are closer to a visual human interpretation (Huiping

et al., 2003). The OBIA’s results (extracted objects) can be inte-

grated within vector GIS more easily than classified raster maps

(Walter, 2004). The OBIA can be applied on different satellite

images. However, it has proven to be particularly appropriate for

VHSR imagery especially in urban areas ( Jacquin et al., 2008;

Campbell, 2007; Mo et al., 2007; Herold et al., 2003; Shackelford

and Davis, 2003; Rego and Koch, 2003; Bauer and Steinnocher,

2001; Hofmann, 2001b). The classification of VHSR images of urban

environment can show some imprecision due to the nature of

these images and the studied objects. In fact, satellite images’ pix-

els can correspond to several objects of different natures. This mix-

ture causes imprecision in the classification of these pixels. With

VHSR images, the problem is less important but still present. Also,some different objects can yield close or similar spectral responses.

This problem is greater in urban areas. Several objects of different

classes yield the same spectral responses. This is due to the use of

the same building materials and/or by the low spectral resolution

of the images (Herold et al., 2004).

The researches on object extraction from VHSR images in urban

areas which applied the OBIA approach are various. They differ

according to the objects to extract and also to the methods used

in each stage. The extraction can concern only one object class (like

building (Lee et al., 2003), Roads (Repaka et al., 2004)) or try to

interpret the whole image (Shackelford and Davis, 2003). The used

classification methods can vary from the more conventional, using

only spectral information (Myint et al., 2011), to the more complex,

based on external knowledge (Forestier et al., 2012; Bouziani et al.,2010). In OBIA approach, the integrated knowledge can be related

to the objects’ characteristics (spectral, geometric and contextual

properties, relationships between objects, etc.), to information ini-

tially relative to the extraction (object models, constraints, etc.) or

to the used data (date and position of the sensor) (Baltsavias,

2004). Adopting a classification method based on knowledge

would give the opportunity to take more information into account.

This allows better discrimination between object classes and effi-

cient extraction of objects (Campbell, 2007). Rule-based systemsbelong to knowledge-based methods that simulate the human rea-

soning mechanism and translate knowledge through decision rules

(Tso and Mather, 2001). Fuzzy logic can also be integrated in clas-

sification methods in order to resolve knowledge representation

and classification problems in complex environments (Han et al.,

2005). Thus, fuzzy rules consist of a set of fuzzy expressions allow-

ing the evaluation of specific attributes. In comparison with a clas-

sical rule, the response to a fuzzy rule is given with a degree that

expresses the satisfaction of this rule, simultaneous application

of several rules is allowed and an object can have different mem-

bership degrees to the studied classes (Dubois et al., 2007). The fi-

nal decision can be taken according to the rule to which the

membership degree is maximal.

Several studies have used fuzzy rule base in OBIA approach to

extract urban object from VHSR. A review of these studies can be

found in Weng (2012) and Blaschke (2010). They have shown high

extraction precisions. However, during their process, values of

parameters and thresholds were set manually. One of the recent

OBIA research is directed towards the automation of image pro-

cessing (Blaschke, 2010). This should concern both segmentation

and classification methods used during OBIA process. For segmen-

tation, automatic technique should allow the creation of image ob-

jects without setting any parameters or homogeneity threshold.

For a fuzzy rule base, an automatic solution will allow the general-

ization of decision rules on other images of different types without

reformulating other rules more adapted to the new context. For-

mulating rules based on human expert knowledge is not always

easy. Ascertaining thresholds and weights of rules is usually left

to the human user or based on training data (Walter, 2004; Ishibu-chi et al.,1992; Puissant et al., 2006).

In this paper, we propose a methodology for automatic extrac-

tion of urban objects from VHSR satellite images through an ob-

ject-based image analysis approach. The proposed solution

requires no input data other than the studied image. Not input

parameters are required. Segmentation is conduct by a nonpara-

metric cooperative technique. The extraction is based on a fuzzy

rule base adapted for interpreting VHSR images in urban areas.

The extracted objects are organized in layers with information on

the precision and certainty of their extraction. This paper is orga-

nized as follows. We describe first the proposed methodology.

We present then the test data as well as obtained results. A last

section will present the analysis and discussion of these results.

2. Proposed approach

We proposed a new approach for automatic extraction of ob-

jects from VHSR images. The objects of interest concern principal

urban object and are presented in Table 1. The proposed approach

adopts object-based image analysis principle and is constitutes of

two principal steps: creation of primitives from pixels and creation

of objects from primitives. The primitive corresponds to an inter-

mediate state between the pixel and the object to extract. It con-

sists of a group of homogenous adjacent pixels. The first step is

conducted by segmentation technique and the second by fuzzy

rule base. These two steps are performed automatically without

need to introduce parameters. The extracted objects are organizedin layers by classes. Individual layers are overlaid to produce a final

172 I. Sebari, D.-C. He / ISPRS Journal of Photogrammetry and Remote Sensing 79 (2013) 171–184



output map of extracted urban objects. Information on the quality

of the extraction is also provided. Fig. 1 presents the concept of our

approach.

2.1. Creation of primitives from pixels

Segmentation technique is used to create the primitives from

pixels. The image segmentation algorithm used in this study fol-

lows the approach given in Sebari and He (2009) where the seg-mentation requires no parameters and no input data other than

the images to be processed. It is based on cooperation between re-

gion-growing segmentation and edge segmentation. The segmen-

tation adopts sequential region-edge cooperation. The edge

segmentation is performed first on panchromatic band and inte-

grated into multispectral region growing segmentation as addi-

tional criteria for seeds selection and for segmentation criteria

definition. The approach uses a spectral homogeneity criterion

whose threshold is adaptive. It varies across the image depending

on the object to be segmented and its neighborhood. It is more

appropriate than a single threshold to apply to the entire image

especially for complex images like VHSR images. The Fig. 2 pre-

sents the principle of the adopted segmentation.

The threshold of spectral homogeneity is calculated automati-cally for each new segment in every spectral band during the pro-

cess of segmentation: Once the seed is chosen and before

aggregating the pixels, a window of analysis is centered on it and

the spectral values of pixels contained in this window are consid-

ered to elaborate a frequency histogram for differentiated values.

For this purpose, a band differentiation algorithm is used to deter-

mine the absolute maximum difference between a pixel and its

neighbors for each pixel:

dv ði; jÞ ¼ max16k61

16l61

jv ði; jÞ v ði þ k; j þ lÞj ð1Þ

With dv(i, j) is the differentiated value at the pixel (i, j) and v (i, j)

is the value of pixel (i, j). In homogenous areas, dv has small values,

whereas dv takes larger values in the boundaries between regions.The overall shape of the histogram is bimodal (Fig. 3): Since more

pixels are inside objects than boundaries, the first peak corre-

sponds to pixels inside the objects and the second represents the

pixels at the boundaries. If dv is considered as the spectral homo-

geneity factor h, the threshold T is considered as the valley be-

tween the two peaks. The automatic detection of this valley is

done according to a modified technique of Zack et al. (1977). The

valley corresponds to the point in the histogram that is farthest

from the straight line joining the two peaks of the histogram.

A pixel may be assigned to a segment if it satisfies in the n

bands of the image the following condition:

fðh1 < T 1ÞAND . . .AND ðhb < T bÞAND . . .ANDðhn < T nÞg;

with; b ¼ f1; . . . ;ng ð2Þ

After the segmentation, the primitives are transformed into vec-

tor format and represented by polygons. Our choice of the vector

mode is justified by many reasons:

- Easy definition of geometric properties.

- Explicit topology between the different objects to define con-

textual properties.

- Possibility to describe a segment through several attributes

stored in a database.

- Easier to integrate the extracted objects’ layers in existing geo-

graphic database.

- Possibility of overlaying the polygons’ layer with other layers in

raster or vector format.

- Easier to compare to others geographic database.

2.2. Creation of objects from primitives

The step of passing from primitives to object is conducted by

applying a fuzzy rule base. This fuzzy rule base contains a knowl-

edge used by a human photo-interpreter to identify urban objects.

Table 1

Objects of interest.

Level I Level II Level III

Natural classes Vegetation Lawn

Forest

Tree

Bare soil Bare soil

Water River

Lake

Man-made classes Road network Road

Parking lot

Building Building

E x t r a c t e d

i n f o r m a t i o n

P r o p o s e d A u t o m a t i c

O b j e c t e x t r a c t i o n a p p r o a c h

I n p u t d a t a

B G R NIR

Multispectral

segmentation Fuzzy rule base

Object classes M e m b e r s h i p d e g r e e

Vegetation Building Road Parking lot

Segmented image

PAN

Information on

extraction quality

Fig. 1. Proposed automatic object extraction approach.

I. Sebari, D.-C. He / ISPRS Journal of Photogrammetry and Remote Sensing 79 (2013) 171–184 173



The methodology adopted to establish the rules is as follow:

knowledge modeling, fuzzy rule base creation and assignment of

objects to classes.

2.2.1. Knowledge modeling

The purpose of this step is to model the knowledge that an ex-pert uses in order to identify urban objects. Five photo-interpreta-

tion keys (Paine and Kiser, 2003) were used to define objects’

properties: size, shape, color (spectral response), texture and sha-

dow. We have considered spectral, textural, geometric and contex-

tual properties to describe studied objects. Then, a quantifiable

attribute is associated to each object property. A quantifiable attri-

bute is defined by a mathematical formulation. The choice of attri-

butes is based upon those used in the literature and validated by

tests. The Table 2 presents the quantified attributes adopted for

each property.

Then, we have done a discriminative analysis which purpose is

to define, for each attribute, a discriminative threshold upon which

the attribute will characterize the associated property. The analysis

consists in studying the mathematic formulation and variation of each attribute. Afterwards, two approaches were followed to

define attribute’s discriminative thresholds depending on whether

the studied attribute is dependent or not on the image. For the

attributes that are independent on the image (like elongation in-

dex, compactness index, etc.), the thresholds are determined based

on their use in the literature and on tests. For the attributes

depending on the image, methodologies are proposed to automat-

ically define the corresponding discriminative thresholds. The con-

cerned attributes are the brightness index (shadow property) and

area (large property).The methodology used to automatically determine the bright-

ness index threshold is based on the histogram of frequencies of in-

dex’s values in the studied image. The threshold is chosen as the

first valley in the histogram (Fig. 3). This is justified by the fact that

shadow areas in VHSR image present low spectral values in the

four bands. We consider that, for an urban area image, and on

the frequency histogram of brightness index, the first peak reflects

shadow areas. It reflects also water or low albedo materials. But,

since the objects will be described by multicriteria rules, the use

of other attributes will allow the discrimination between shadow

and the others objects.

The automatic extraction of the brightness index threshold is

performed by applying the proposed algorithm: (1) Searching

automatically first for the value corresponding with the first peak(first maximal value), (2) searching automatically thereafter for

the value of the threshold that satisfies the criteria: its brightness

index value is higher to the one of the first peak and it is comprised

within two values whose frequencies are higher than the fre-

quency of this value.

The area index is used to describe large size objects. Defining

area index threshold is based on the knowledge that, on an urban

VHSR image, there are more objects of small and medium size than

large-size objects. Thus, the frequency of an image’s objects’ area

values histogram looks like described in Fig. 4. The automatic

extraction of this threshold is performed by using a modified ver-

sion of the triangle technique proposed by Zack et al. (1977). It is

performed through an algorithm that searches for the value corre-

sponding to the farthest point on the line joining the greatest peakand the last point in the histogram with the maximum area value.

Automatic determinationof spectral homogeneitythresholds for n bands

Add to the region'sboundary

New segment

For each pixel in the list

Yes

No

Segment

Look for the 8unassigned

neighboring pixels

Add to the lis t of pixels to check T1, …,T

n

Add the pixel to theregion

Seed

Check the spectral homogeneity criterion and the edge criterion

≠

Look for the 8 unassigned

neighboring pixels

Check the adjacency criterion

hb < T

b | b={1,…,n}

edge pixel

Fig. 2. Adopted segmentation approach (Sebari and He, 2009).

Differentiated

values (dv)Threshold selected

Objects

Edges

P i x e l s

Fig. 3. General shape of the histogram of the differentiated values.




The equation of the straight line connecting the two points

P 1( A1, f 1) and P 2( A2, f 2) is:

a x þ b y þ c ¼ 0

with: a ¼ f 1 f 2 A1 A2

, b ¼ 1 and c ¼ f 1 a A1

The distance DP i between each point P i of the histogram and this

line is calculated:

DP i ¼a xP i þ b yP i

þ c ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffia2 þ b

2p

The histogram valley corresponds to the point of the histogram

whose distance DP i is maximal. This technique offers the advantage

of valley detection being unaffected by histogram irregularities.

Also, we reminder that the detected threshold will be used in fuzzy

rule which will take in account the imprecision related to its

determination.

Adopting these methodologies to define attribute’s thresholds is

according to the purpose of our approach: extract object from im-

age without set up of any parameter. The possible imprecision

associated to these methodologies will be take in count by usingfuzzy logic during rules formulation.

2.2.2. Fuzzy rule base creation

The use fuzzy logic is considered appropriate for problems re-

lated to urban areas images (Longley et al., 2001): uncertainty

associated to the objects’ boundaries, fuzziness in the definition

of classes, possibility to belong to more than one class. One of its

benefits is the possibility to express the membership degrees of ob-

ject to different classes and the uncertainty related to objects

extraction process.

Several reasons justify our choice for fuzzy rule base: in our

case, no a priori knowledge related to the extracted object is used.

The rules are formulated based on photo-interpretation’s descrip-

tion; the fuzzy logic allows the formulation of knowledge given

in natural language with vague and imprecise expressions. Also,

it avoids the use of rigid limits for class definitions and introduces

thresholds uncertainty; what will reduce the imprecision related

to the adopted threshold determination methods. With this a lack

of knowledge, the extracted objects should be provided with infor-

mation of the precision of their determination; it is possible with

fuzzy logic which allows the use of membership functions and of

degree of truth attributed to each extracted object (Bouchon-Meu-

nier and Marsala, 2003; Dubois et al., 2007).

Each object’s property is described by a fuzzy proposition andconsidered as a fuzzy set characterized by a membership function.

The membership degree is determined according to the value of

the attribute associated to the property. Each object’s class is de-

scribed by a fuzzy rule that expresses the relationship between

the class and the properties that describe it. The satisfaction degree

of a class rule is determined according to the membership degrees

of properties.

2.2.2.1. Fuzzy propositions on properties. Fuzzy proposition on prop-

erty correspond to a fuzzy formulation of the condition that the

quantifiable attribute must satisfy in order to discriminate the ob-

ject property. The formulation of a fuzzy proposition on an object

property starts by the description of the property by a fuzzy subset.Generally, a fuzzy proposition is like ‘‘ X is P ’’, with X being a vari-

Table 2

Correspondence between properties and quantifiable attributes.

Property Quantifiable attribute Mathematical formulation

Spectral attributes Vegetation Normalized Difference Vegetation

Index (NDVI)NDVI ¼ NIR R

NIR þR

Shadow Brightness index I br ¼ 16 ðB þ V þ 2R þ 2NIR Þ

Water Near-infrared (NIR) spectral

response

NIR

Textural attributes Texture Homogeneity index

HOM ¼Xn

i¼0

Xn

i¼0

1

1 þ ði jÞ2 hc ði; jÞ

Geometric

attributes

Elongated shape Elongation index I elongation ¼ 4p AP 2

A and P are the area and the perimeter of the exterior border

Compact shape Compactness index I compactness ¼ 4p AHull

P 2Hull

AHull and P Hull are the area and the perimeter of the convex hull polygon

Convex shape Convexity index I convexity ¼ A AHull

A is the object’s area and AHull is the area of the convex hull polygon

Large size Area A = Area of the object

Contextual

attributes

Neighboring object Adjacency Determines the neighboring objects that share a part of their limits

Direction between twoobjects

Azimuth Az 12 ¼ arctg X 2 X 1Y 2Y 1

( X 1, Y 1) coordinates of the centroid of object 1

( X 2, Y 2) coordinates of the centroid of object 2

Elevated object Adjacency + Shadow+ Azimuth Determines if object is adjacent to shadow zones in the direction of the

sun

Frequencies (f)

f 1

A2

f 2

Area

(A)

Area Threshold

DPi

P1

P2

A1

Fig. 4. Automatic detection of area’s threshold.




able and P is the fuzzy subset studied. The subset is defined by a

membership functionl p that associates to each value x of the attri-

bute a real value l p( x) in the interval [0,1]. This value l p( x) trans-

lates the satisfaction degree of the property P by an object. The

higher the membership degree is, the more the property is satis-

fied, and vice versa.

For the determination of the satisfaction degree of the opposite

property, the ‘‘complement’’ operator is used. For example, if lc ( x

)is the membership degree for the ‘‘compact shape’’ property, the

membership degree to the ‘‘non-compact shape’’ property lnon c ( x)

is equal to:

lnon c ð xÞ ¼ 1 lc ð xÞ ð3Þ

There are many types of membership functions: continuous

(e.g. Gaussian or sigmoid functions) or piecewise linear (e.g. trian-

gular or trapezoidal functions) (Zadeh, 2003). The latest type is a

simple form which contains straight line segments. We have

choose this type of membership functions since it will allows to

represent the knowledge collected during the modeling step. Thus,

the shapes of membership functions are defined with relation to

information related to attribute’s mathematic formulations and

threshold’s values. The shape of the membership function is spe-

cific for each property.

The methodology starts by specifying the intervals for which

the associated attribute values characterize the property, as well

as those for which the property is not validated. Between those

intervals, a transition interval, defined with relation to the attri-

bute’s threshold, reflects a gradual membership and minimizes

the imprecision related the attributes’ thresholds determination.

Fig. 5 represents the overall shape of the used membership

function.

2.2.2.2. Fuzzy rules on object classes. An object class rule constitutes

a conjunction of propositions on the properties describing this

class. Generally, it follows this form:

IF X 1 is P 1 AND . . .

AND X i is P i AND . . .

X n is P nTHEN Y is C ið4Þ

where « X i is P i» is a fuzzy proposition on a property and «Y is C i» the

conclusion translating membership to the class C i. The degree of

satisfaction of a rule is obtained by using a fuzzy aggregation oper-

ator which aggregates the membership degrees like a logical ‘‘AND’’.

We adopt the following operator:

lC ið xÞ ¼

Yai¼1

lP ið xÞ

!1=a

ð5Þ

where lC ið xÞ is the membership degree of object x to class C i,

lP ið xÞ is the satisfaction degree of object x to property P i and a is

the number of fuzzy properties propositions that constitute the

class rule. We have chosen this operator because since the aggrega-tion of the rules is performed in a conjunctive way, if one of the

rules concludes that the membership degree to the class C i is 0,

then no other rule can change this conclusion, and the class C i will

be considered impossible. In other terms, if an object does not sat-

isfy an object class’ property, it will not be affected to this class. Its

satisfaction degree will be null. Also, this operator verifies the nor-

malization condition (Eq. (6)); the comparison of the satisfaction

degrees of the various class rules is possible.

sup x2 Apð xÞ ¼ 1 ð6Þ

The membership function of each class is represented by a sin-

gleton. The membership degree is a real value in the interval [0, 1].

Fig. 6 presents a simplified example that illustrates the principle of

determination of class membership degree. Class A is described by

two properties ‘‘elongated shape’’ and ‘‘non-compact shape’’. The

fuzzy rule of this class is:

IF object shape is long AND object shape is non-compact THEN

object belongs to class A

The studied object presents values for the attributes associated

to these properties: the elongation index for the elongated shape

propriety and the compactness index for the non-compact shape

propriety. First, the satisfaction degrees of the object to the two

fuzzy proprieties propositions are determined, lL( x) and lNC ( x).The membership degree of the object to the class A is obtained

by aggregating the two degrees with the fuzzy operator obtained

through the Eq. (3): lRoad( x) = (lL( x). lNC ( x))½.

The following rules were used:

WATER _RULE = {(RS is water)}

TREE_RULE = {(RS is vegetation) AND (texture is rough)}

LAWN_RULE = {(RS is vegetation) AND (texture is non rough)}

BARE_SOIL_RULE = {(RS is bare soil)}

BUILDING_RULE = {(RS is non-vegetation) AND (RS is non-bare

soil) AND (RS is non-water) AND (elevated object) AND (Shape

is compact) AND (shape is convex)}

ROAD_RULE = {(RS is non-vegetation) AND (RS is non-bare soil)

AND (RS is non-water) AND (Object non-shadow) AND (Object

non-elevated) AND (shape is Elongated) AND (size is large)}PARKING_LOT _RULE = {(RS is non-vegetation) AND (RS is non-

soil) AND (RS is non-water) AND (Object non-shadow) AND

(Object non-elevated) AND (large size) AND (shape is

compact) AND (shape is convex)}

2.2.2.3. Auto-learning process. Since no knowledge on the real pro-

prieties of objects is used during rules formulation, we integrate an

auto-learning process to automatically retrieve information about

potential objects. The retrieved knowledge is integrated into new

rules in order to extract more objects. This process starts after

the application of the defined fuzzy class rules. It retrieves infor-

mation from potential objects that are defined as objects having re-sponded to the fuzzy class rules with high membership degrees.

They are considered as representative of their class and conse-

quently the values of their attributes characterize the objects of

this class. The auto-learning process retrieves spectral and geomet-

ric attribute’ values and integrates them in a library created for this

purpose. The spectral information is used to evaluate the spectral

membership of each object to the class’ spectral library. The geo-

metric information is integrated in order to evaluate the geometric

conformity with potential objects. Contextual information is used

with the retrieved information to define new class rules. This pro-

cess is applied for man made classes: building , road and parking lot .

For the road class, the new rule is defined in order to connect the

extracted road network. Thus, the object, in addition to having

membership to the road spectral library, must be adjacent to an al-ready extracted road object. For the parking lot class, the corre-

0

1

0 Threshold 1

Attribute

µ A

Fig. 5. General shape of the used membership function.




sponding rule includes membership to the spectral and geometric

parking lots libraries. The new rules for the building class include

membership to the spectral and geometric buildings library’s and

shape’s compactness condition. In order to take more advantage

of the collected spectral library, the primitives that do not belong

to the building, road or parking lot classes, and have membership

to the spectral libraries, are extracted as impervious surfaces.

2.2.3. Assignment of objects to classes

After application of the fuzzy rule base, each object has a mem-

bership degree that varies from 0 to 1 to each class.

After applying the rule base, an object has membership degrees

to the all studied classes (varying from 0 to 1). In order to interpret

theses results and to take a decision, we have adopted some no-

tions of the possibility theory. The possibility theory is linked with

the fuzzy sets theory and allows to reason on imprecise or vague

knowledge.

If C is the set of classes, a possibility distribution p is defined as

a function that attributes a possibility coefficient p x(C i) to each ele-

ment of C . This coefficient represents the possibility that an object

x belongs to a class C i. If p x(C i) = 0 then the object cannot belong to

the class C i. If p x(C i) = 1 then it is possible (but not certain) that theobject belongs to the class C i.

The interest of using possibility distribution is the definition of

measures that will estimate the quality of the extraction. We have

used possibility and necessity measures. These measures quantify

the imprecision and the uncertainty of the extraction. A possibility

measure is elaborated by considering, for any part of A, the coeffi-

cients of the elements of A that compose it (Dubois et al., 2007):Yð AÞ ¼ supu2 Ap xðuÞ ð7Þ

The value of G( A) corresponds to the element(s) of A having the

greatest possibility degree according to p x. It satisfies the following

max-decomposability characteristic property (Dubois et al., 2007):

Pð A [ BÞ ¼ maxðPð AÞ;PðBÞÞ ð8Þ

The possibility measure of an event estimates to what extent it

is possible. But it is insufficient to inform if an event will be real-

ized. Information about the complement event is useful. The neces-

sity measure can be defined as the impossibility of A , the

complement of A (‘not A ’):

N ð AÞ ¼ 1 Y

ð AÞ ¼ inf uR A

1 p xðuÞ ð9Þ

Thus, P( A) corresponds to the degree with which it is possible

that A is true, N ( A) as the degree with which it is certain that A is

true. The necessity measure is an evaluation of the certainty (Bou-

chon-Meunier and Marsala, 2003). By using these two measures,

the imprecision and the uncertainty of the object extraction pro-cess is quantified and consequently its quality is estimated.

The following example clarifies these two measures of possibil-

ity and necessity. If C is the set of classes {Building; Road; Parking

lot}. We suppose that, after applying the fuzzy rule base, an object

x presents the following membership degrees {1/Building; 0.1/

Road; 0.5/prkg_lot}. The object x presents the possibility distribu-

tion illustrated by Fig. 7. The object is assigned to the building class

because it presents the greatest possibility degree. The certainty

degree of this assignment is obtained through the necessitymeasure:

N ðC BuildingÞ ¼ 1 Y

ðC BuildingÞ

¼ 1 maxY

ðC RoadÞ;Y

ðC prkg lotÞ

ð10Þ

N ðC BuildingÞ ¼ 1 Y

ðC prkg lotÞ ¼ 1 0:5 ¼ 0:5 ð11Þ

So, the certainty that the object x belong to the building class is

0.5. Thus, the object x is a building with a precision degree of 1 and

a certainty degree of 0.5. It is 50% certain that the object x is 100%a

building.

The conflict between two classes regarding an object is defined

by the difference between its membership degrees to two classes.

The smaller is the difference, the higher is the conflict. Ideally, a

well-extracted object will present very high precision and cer-

tainty degrees and a low to null conflict with the other classes.

The following cases can be found:

- If a unique class C i such as p X (C i) = 1 exists, the class C i is the

most certain since N (C i) > 0 and N (C j) = 0 for all j – i.

- If several C i are such as p X (C i) = 1, no decision can be taken

between these classes: the rule base does not have enough

knowledge to discriminate. However, if the classes with possi-

bility degrees equal to 1 correspond to a significant class subset,

then the certainty of this subset is strictly positive.

- If p X (C i) = 1 everywhere, then there is a total uncertainty on the

class.

This information on certainty and precision of the object extrac-

tion is given with the object extraction results. Indeed, the pro-

Object x I Comp

(x)

Road

F u z z y

a g g r e g a t i o n

o p e r a t o r

ElongationIndex

µ Road

( object x )

µL µNCµclass A

µL(x)

µNC

(x)

Non-compact shapeElongated shape

CompactnessIndex

I Elong

(x)

Class : «Road »Property :

Fig. 6. Object’s membership degree to a class.

Building Road Parking lot

π

1

0

Fig. 7. Possibility distribution.




posed approach allows the organization of the extracted objects in

layers by class. Each object is described by a set of spectral, geo-

metric and contextual attributes and also by precision and cer-

tainty information related to the extraction quality.

2.3. Evaluation of the results

The evaluation of extracted objects was performed by compar-ison with reference data representing ground truth, according to:

extraction accuracy assessment and geometric quality assessment.

For accuracy assessment, two indices were used first to appre-

ciate the overall objects extraction with relation to reference data:

completeness and correctness. The completeness index represents

the percentage of the reference objects’ area that is extracted. The

correctness index is the percentage of the extracted objects’ area

that is correctly extracted.

Completeness ¼ S CE

S Rð12Þ

Correctness ¼ S CE

S E

ð13Þ

With S CE being the objects’ area correctly extracted, S R the total area

of the reference objects, and S E being the total area of the extracted

objects.

We also produced error matrices according to percentage of the

area of objects. Correctness, completeness, Producer’s accuracy and

User’s accuracy were generated. For man made classes, the accu-

racy assessment has also been performed according to the number

of objects for buildings and parking lots classes, and according to

object length for road class.

For geometric quality assessment, two indexes are used to ana-

lyze the geometric quality of extracted objects: area ratio and po-

sition error. The area ratio (R A) yields information on the

percentage of the area accurately extracted for each object. It is de-

fined as follows:

R A ¼ AðReal Objet \ Extracted ObjetÞ

AðReal ObjetÞ ð14Þ

where A is the area. The position error (E p) is related to the mean

distance between extracted objects and their corresponding objects

in a reference data. It is determined according to characteristic

points in the two data. For buildings, the characteristic points cor-

respond to building corners, and for roads, they are chosen along

road axes. The position error (E p) is determined by the formula:

E p ¼

Pnk¼1

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi xext xref ð Þ2 yext yref ð Þ2

q n

ð15Þ

where n is the number of the characteristic points, ( xext, yext) corre-spond to extracted point’s coordinates and ( xref , yref ) are coordinates

of the corresponding characteristic point on the reference layer.

3. Application

3.1. Data and study area

The proposed approach was applied to Ikonos image of the city

of Sherbrooke (Canada) acquired on 20 May 2001. We selected

three subsets from Ikonos image presenting different land use (ob-

ject’s size and density) in order to evaluate the approach on differ-

ent urban environment. In addition, we selected another Ikonos

image acquired on November 2004 over Sherbrooke (Canada) to

evaluate the effectiveness of the approach and its transferabilityon the future. We intentionally selected an unchanged zone from

the two dates to examine if the approach was able to extract the

same objects. This way, the eventual observed changes in the re-

sults will be attributed to the approach. The reference data (ground

truth) was generated by photo-interpretation of the studied image.

The Ikonos image contains four spectral bands (blue, green, red

and near-infrared) and a panchromatic band with spatial resolu-

tions of 4 m and 1 m, respectively. In order to mutually benefit

from multispectral information and very high spatial resolution,the four bands have been individually fused with the panchromatic

image. The image fusion method used is the one proposed by He

et al. (2004). This method allows preserving faithfully the spectral

aspect of the low resolution image while integrating the spatial

information of the high resolution image.

3.2. Results and discussion

Fig. 8 shows the results on three extracts. 80% of the total refer-

ence area was extracted (completeness index) (Table 5). The cor-

rectness rate was of 78% (Table 3). The confusion matrices of the

three study sites are given by Tables 4–6 according to classes of le-

vel II of Table 1. The results show that the correctness rates for the

natural classes are high between 84% and 90%. This is due to their

distinct spectral responses. The spectral rules were the most

important to discriminate the natural classes and to differentiate

them from the man made classes. The main source of errors is re-

lated to the photo-interpretation conducted to create the ground

truth: it was difficult during photo-interpretation to identify some

vegetation areas due to their low contrast. For the man made clas-

ses, the results are encouraging with 80% as mean correctness rate.

For building class, 80% of the total reference area was correctly ex-

tracted. According to the number of extracted building, the results

analysis has revealed completeness rates of 93% for site 1, 77% for

site 2 and 96% for site 3, and a mean of correctness rate of 93% (Ta-

ble 7). The auto-learning process has contributed to reach these

values. It has allowed the extraction of more building (For example,

8 correct buildings were extracted in site 2 through the auto-learn-

ing rules). Some buildings have been omitted by the approach be-cause they not have the properties defined for the building class.

Even with the auto-learning process, their spectral responses did

not correspond to those recorded in the retrieved library. Another

reason of this omission is that the distinction of these buildings

with relation to their neighborhood was difficult during the stage

of the creation of the primitives (segmentation). They were fused

with their neighborhood and thus did not have the characteristics

of the building class objects. The objects incorrectly extracted as

buildings correspond to artificial areas that have similar geometric

and contextual properties. The complete and accurate extraction of

buildings objects faces two major problems: the confusion of

buildings with the neighborhood and the heterogeneity of the

buildings roofs.

Considering the road network class, the correctness rates areabout 75%. The false classified areas from the road network repre-

sent less than 4%. An analysis of the results of the two separate

subclasses road and parking lot is performed (Table 7). For the road

class, 80% of the total length of the roads for the studied sites has

been extracted. Their elongated shape is an interesting property for

their extraction. For a homogenous road, the approach allows its

quasi-complete extraction. Some omissions are due to the hetero-

geneity of roads’ surface and to projected shadows on roads either

by trees or by buildings.

For ‘‘parking lot’’ class, the confrontation with the ground truth

has showed that the quality of the extraction depends on the stud-

ied site. For some sites, all lots were extracted while for some oth-

ers, some parking lots have been omitted (site 2). Two reasons can

be identified. First, the definition of the objects of this class in therule base uses the property large size object. The approach allowed




the extraction of the objects respecting this characteristic, but in

site 2, parking lots are located in residential areas and are of small

sizes. The second reason is the segmentation’s results. In some

case, due to presence of cars, the shape of the segmented parking

lot becomes elongated instead of compact and can be classified

as road. Also, two adjacent road and parking lot objects can form

one segment since they are built with the same material and ex-

tracted as one object.

The objects of the building and road classes are extracted with

an average of 80% of their area (Fig. 9). The objects of the parking

lot class have less important rates (average of 60%). This is due to

the complexity and the heterogeneity of these objects, as well asby the presence of details (for example cars). For the position error,

the buildings present high spreads (higher than 11 m in the case of

site 3). For roads, a maximal value of 6 m was observed. These

spreads are due to: in the case of road objects, the central axe of

road can be offset due to the modified boundaries by shadows of

trees or buildings; for buildings, the corners of extracted objects

Fig. 8. Application of the proposed approach on Sherbrooke’s Ikonos image extracts (Site 1, Site 2, Site 3) (a) image extracts (b) extracted objects (c) reference map.

Table 3

Extraction quality index.

Indices Site 1 Site 2 Site 3

Completeness (%) 79 83 78

Correctness (%) 81 83 73




can be offset due to incorrect delimitation by the approach and the

confusion between some roofs and fronts.

3.2.1. Shadow extraction

In order to evaluate the shadow detection performance, we

have compared extracted shadow areas to reference shadow areas

(created by photo-interpretation) for the extract 3 (Fig. 10). 92% of shadow areas were well extracted. Producer’s accuracy (about 28%)

Table 5

Confusion matrix – site 2.

Extracted objects Reference data

Vegetation Bare Soil Water Building Road network Correctness rate Producer’s accuracy

Vegetation 84 0 3 2 2 81 19

Bare soil 0 0 0 0 0 – –

Water 0 0 90 0 0 99 1

Building 0 0 0 78 0 81 19Road network 2 0 0 4 71 72 28

Completeness 84 – 90 78 71

User’s accuracy 16 – 10 21 29

The values in the error matrix are presented in % of object’s area.

Table 4



Vegetation Bare soil Water Building Road network Correctness rate Producer’s accuracy

Vegetation 84 0 0 0 1 84 16

Bare soil 0 0 0 0 0 – –

Water 0 0 0 0 0 – –

Building 1 0 0 80 0 84 16

Road network 3 0 0 2 74 78 21

Completeness 84 – – 80 74

User’s accuracy 16 – – 20 26


Table 6



Vegetation Bare Soil Water Building Road network Correctness rate Producer’s accuracy

Vegetation 85 0 0 0 0 71 29

Bare soil 0 0 0 0 0 – –

Water 0 0 0 0 0 – –

Building 0 0 0 77 2 78 22Road network 0 0 0 1 81 75 25

Completeness 85 0 0 77 81

User’s accuracy 15 0 0 23 19


Table 7

Road and parking lot classes’ objects’ extraction quality indices.

Indices Road Parking lots

Site 1 Site 2 Site 3 Site 1 Site 2 Site 3

Completeness (%) 84 96 90 100 56 70

Correctness (%) 90 86 75 100 56 77

% of road length % of number of objects

0

20

40

60

80

100

Site 1 Site 2 Site 3 Site 1 Site 2 Site 3

A r e a R a t i o ( % )

Building Objects

0

2

4

6

8

10

12

P o s i t i o n E r r o r ( m )

Building objects

Fig. 9. Geometric quality assessment.

Extracted shadow Real shadow (by photo-interpretation)

Fig. 10. Comparison between extracted and reference shadow areas.




is due in major part to the self shadows. The User’s accuracy is esti-

mated to 8%. It is due to some low albedo materials. The advantage

of using fuzzy logic is that these areas present membership degrees

to shadow and also to the real class. The use of others attributes

(spectral, geometric and contextual attributes) minimizes the er-

rors of urban objects extraction. Even though detecting shadow

areas was useful for the identification of elevated objects (specially

building), some problems were observed: the projected shadowhas prevented the extraction of some parts of roads and parking

lots; the self shadow (the part of the object that is not illuminated,

i.e., the façade of the building) is not always detected, which has

cause in some cases the incorrect extraction of the geometric form

of objects.

3.2.2. Membership degrees

The objects membership degrees to final classes are generally

high (greater than 0.6). The mean degrees are about 0.8 for the veg-

etation class, 0.8 for the building class, 0.85 for the road class and

0.65 for the parking lot class. The membership conflict analysis has

revealed that the objects of natural classes present membership

degrees only to their corresponding class, whereas non-natural ob-

ject classes can present membership degrees to more than one

class. The objects that have a higher membership degree belong

to the appropriate class. The analysis of the precision and certainty

degrees of their extraction has revealed that more than 60% of the

objects extracted have a precision degree higher than 0.6 and a cer-

tainty degree higher than 0.8 (Fig. 11). For the certainty degrees,

they are higher than 0.8 and can reach a value of 1. ‘‘Typical’’ ob-

jects present high degrees of precision and certainty and low or

null conflicts with others classes.

For all the classes, there are objects that present membership

degree equal to 1 which means that the class rule is descriptive

of the studied object. Also, no object has presented membership

degree equal to 1 for all the classes. We can conclude that the rule

base contains enough knowledge to discriminate between the

objects.

3.2.3. Thresholds determination

In order to evaluate the adopted methodology for automatically

determinate attributes’ thresholds, we have conducted a sensitiv-

ity analysis by introducing variations to the determined thresholds

values. The corresponding rule is then applied and the correctness

rate, Producer’s and user’s accuracies are determined. Table 8 pre-

sents the example of results of this analysis in the case of bright-

ness index (S ) used to detect shadow areas. Variations DS of 10

were added and subtracted from the threshold’s value.

The analysis shows that the use of obtained threshold yields the

best results (correctness rate of 92% with producer’s accuracy of

28%). Using ‘‘S + DS ’’, the correctness rate is the same but the pro-

ducer’s accuracy is greater (40%) while with ‘‘S DS ’’, even if the

producer’s accuracy is lower, the User’s accuracy is still important(23%).

The determination of thresholds is very ‘‘sensitive’ task. We

think that using threshold in rigid way is not adequate to our case

(for example, object with a area of 700 m2 is considered large and

another with an area of 701 is not?). So, using fuzzy threshold is in

our opinion more interesting. The proposed approach uses many

thresholds which are automatically derived. But, we should precise

that the automatically determined thresholds are used as approx-

imate values which will be useful to establish the membership

functions. The proposed methodologies for automatic detection

are not ‘‘perfect’’. The imprecision related to the determination of

threshold is considered by fuzzy logic. Indeed, the imprecision of

adopted thresholds is considered by the used membership func-tions: each attribute’ threshold is considered as the crossover point

of the corresponding membership function. A transition interval,

defined with relation to the attribute’s threshold, reflects a gradual

membership and minimizes the imprecision related the attributes’

thresholds determination (Fig. 5).

All the thresholds are important because they are useful to

establish the membership functions. During rule formulation, the

incorrect determination of thresholds may be cause the incorrect

classification of an object if the difference between the correct

and the incorrect values of threshold is important. If not, the object

will have a low membership degree to its real class.

3.2.4. Transferability test

We have also conduct a transferability test in order to verify if,for the same zone, the approach will extract the same objects with-

out any changes in the rule base. The transferability of the extrac-

tion approach was evaluated on two subsets from two different

Ikonos images over the same zone (Fig. 12). The same rule base

was applied on two subsets. The decision rules are not modified

between the two images and no new rule was used. The thresholds

of the attributes dependant of the image are automatically deter-

mined by the approach. The objects were well extracted on both

images. They have the same assignations to the classes on the

2001 and 2004 extracts. The only differences are observed at object

areas and also at shadow zones.

3.2.5. Some limitations

For very large image, the application of the proposed approachwill give satisfied results when extracting typical objects. But, it

Site 1 Site 2 Site 3

0.0

0.2

0.4

0.6

0.8

1.0

D e g r e e o f p r e c i s i o n

Site 1 Site 3Site 2

0.0

0.2

0.4

0.6

0.8

1.0

D e g r e e o f C e r t a i n t y

Fig. 11. Statistics on (a) the precision degrees and (b) certainty degrees of extracted objects on the three study sites.

Table 8

Sensitivity analysis of the brightness index thresholds.

Real shadow (reference data)

Correctness rate (%) User’s accuracy (%) Producer’s accuracy (%)

Extracted shadow

S 92 8 28

S – DS 77 23 12

S + DS 93 7 40




can present some limitations according to the difficulty and the

complexity of the urban environment. The ‘‘typical’’ objects can

be extracted without problems (except the exact geometric shape).A typical object can be for example a building that presents a com-

pact form and a cast shadow. Some buildings in dense zone are

contiguous and it will be difficult to extract individual building.

Also, for roads, on some images, they are hided by building and

shadows. Their extraction is not going to be exhaustive and com-

plete. The extraction results will depend on the segmentation re-

sults. The used algorithm used an adaptive threshold when

segmenting each segment. The algorithm presents satisfactory re-

sults. But, in dense environment, the algorithm may confuse some

objects presenting same spectral responses and consequently pro-

vide one segment.

4. Conclusion

This paper presents a solution for automatic extraction of geo-

graphic urban information from VHSR multispectral images. The

proposed approach is object-based image analysis. It does not

use no auxiliary data or parameter to introduce and does not re-

quire any examples for learning process. The extraction is based

on simple, objective, and transferable rules. Formulating the rules

is performed through the translation of the knowledge used by

the photo-interpreters in order to interpret urban objects from

VHSR image. It used different types of properties. The thresholds

of the attributes, which are dependent on the images and used in

the rules, are automatically determined. Integrating the fuzzy logic

and the possibility theories into the rule base has contributed to

manage the complexity of the subject studied. It allowed to reasonwith imprecise and uncertain knowledge and to give information

on the quality of the extracted objects. The used fuzzy rule base

has the benefit of not requiring any order of application for the

rules. The objects respond to the different rules with satisfactiondegrees ranging from 0 to 1. The order of application has no effect

on the final results. The final decision is taken at the end according

to the precision, certainty and conflict degrees observed on the dif-

ferent classes. The results obtained translate the pertinence of the

rules. A global extraction rate of 80% was observed. The correctness

rates obtained for the building, road and parking lot classes are of

81%, 75% and 60%, respectively.

The challenge was great given the complexity of the studied

area and the particularities of the used images. The automatic

extraction approach proposed has displayed a transferability

power. One of the limitations of this approach is the conformity

of the geometric shape of the extracted objects to the reality.

Enhancing this aspect would be useful for the future integration

of the objects extracted in spatial databases. It would also be inter-esting to study the application of the approach on high spectral sa-

tellite images in order to study the effect of introducing additional

bands on the improvement of mapping accuracy.

Acknowledgments

This work was supported by the NSERC (the Natural Sciences

and Engineering Research Council of Canada) and the PCBF (Prog-

amme Canadien de Bourses de la Francophonie) of CIDA (Canadian

International Development Agency).

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Fig. 12. Transferability study of the approach (a) Ikonos image subset of 2001, (b) Ikonos image subset of 2004, (c) extracted objects from Ikonos 2001, (d) extracted objects

from Ikonos 2004.




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Automatic Fuzzy Object-based Analysis of VHSR Images for Urban 2

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