Monitoring Urban Sprawl from historical aerial photographs and satellite

imagery using Texture analysis and mathematical morphology approaches

Bahaaeddin Alhaddadª, Josep Roca Claderaª and Malcolm C. Burnsª

(bahaa.alhaddad, josep.roca, malcolm.burns)@upc.edu

ª Centre de Política de Sòl i Valoracions- CPSV, Universitat Polítècnica de Catalunya- UPC

Av. Diagonal, 649, 4a planta, 08028 BARCELONA, SPAIN

Abstract

In the last few decades, Urban sprawl refers to the outgrowth of urban areas caused by uncontrolled,

uncoordinated and unplanned growth. The rapidity of urban dynamics has a significant impact on the spatial

patterns associated with the growth and expansion of metropolitan areas. Spain has been urbanizing large

amounts of land, while the total population has hardly increased. Much of the expansion of (sub) urban

development has come at the expense of farmland, forest land, and other areas of open space, mostly as the result

of low-density, sprawling development. Sound land use planning and open space preservation are important

issues in Spain, but currently very little quantitative information is available describing urban sprawl and land

use change. Black and white aerial photography from 1956 and panchromatic satellite imagery from 2005 are

used for studies of growth and change of informal settlements in various landscapes in Spain. The digital data is

Ortho-rectified, corrected for brightness variations and mosaicked using ER Mapper and Global Mapper

software. The texture analysis and mathematical morphology are applied with ENVI and MATLAB.

The focus of this study is to evaluate the capability of using textural analysis for mapping the compact urban

areas of Barcelona city in Spain. The significance of this study is due to (1) the use of historical aerial

photographs and SPOT imagery for monitoring urban land use change; and (2) the research being on developing

Spain cities, which have rapidity of urban dynamics.

1. Introduction

The long-time historical evolution and recent rapid development of Spain, present before us a

unique urban structure. Historical maps and Arial photographs exist for a number of epochs

and are potentially invaluable in the analysis of the changing face of the Spain landscape.

Many of these data exist purely in a hard copy format and as a result a great deal of

potentially valuable information has been lost over time. The collation, scanning and geo-

referencing of these data sources are crucial if further material is not to be lost. Whilst the

digital output of the maps in itself is invaluable, classify and victories data of the urban

settlement would allow a wider, more detailed use of the data. In this article we describe the

application of texture feature extraction approach to classify different images from two main

environments: urban and non urban landscapes. Texture analysis offers interesting

possibilities to characterize the structural heterogeneity of classes. The texture of an image is

related to the spatial distribution of the intensity values in the image, and as such contains

information regarding contrast, uniformity, rugosity, regularity, etc. A considerable number of

quantitative texture features can be extracted from images using different methodologies in

order to characterize these properties, and then can be used to classify pixels following

analogous processes as with spectral classifications. Many texture comparative studies can be

found in the literature, usually carried out by employing standard image databases for the

testing process. Textural information has been an important factor in visual image

interpretation. It takes into consideration the distribution and variation of neighbourhood pixel

values. Haralick et al. (1973) developed a set of texture statistics for image classification

based on Grey Level Co-occurrence Matrix (GLCM) [1]. Studies by Marceau et al. (1990)

demonstrated that these textural statistics were useful to resolve spectral confusion between

land cover classes [2]. Most landscape forms do not have sharp boundaries on remotely

sensed imagery of various scales, but similar types of land cover may have similar spatial

patterns as manifested by gray-level variation in an image. The gray-level variability is

referred to as texture [3]. This paper approaches the measurement of municipal urban growth

from a strictly morphological perspective based on the texture analysis, drawing upon

historical aerial photographs and SPOT imagery dating from 1956 and 2005. The fundamental

goals of this study were:

• The ability of texture analysis approach for the extraction of texture features and change

detection from B&W historical Arial photographs in different environments, analyzing and

assessing the different methodological parameters involved in the process.

• Assess the increased accuracy afforded by texture analysis approach for the monitoring of

key urban development issues both within the confines and beyond the edge of the

municipal Areas.

• Provided a broad indication of the magnitude of change in urban land cover classes

experienced in the Barcelona city in Spain over the study period and to allow a cross-

border comparison of the respective urban regions under consideration.

With the claimed classification accuracy improvement using texture analysis, the study would

incorporate the measures with various texture analysis approaches to detect change detection

areas in several Mediterranean cities in Spain with multispectral satellite imagery and

Historical Arial photographs captured in time 2005 and 1956. As a baseline for the

comparison, a semi-automatic method using edge detection method is introduced to delineate

the boundaries and extracted the urban areas. Finally, a comprehensive comparison between

these approaches are presented with performance percents, visual analysis, a visual

interpretation approach is introduced to delineate the boundary manually for accuracy

masseur.

2. Study area

The chosen area is Barcelona, which is the regional capital of Catalonia, lying in the north-

east of Spain (see Figure 1). The physical limits of Barcelona extend to almost 100 Km2. and

the city had a population of some 1,595,110 inhabitants in January 2007 leading to a

population density of almost 16,000 inhabitants / Km2 [4]. Two data sets were used for the

study. One is subset of an SPOT5 scene, recorded on 2005. It is a fusion product of the

panchromatic band (10m spatial resolution), following the homogeneity of urban settlement

complexity high imagery resolution will not give a good results through mathematical

morphology process. The second data set is from an airborne (black and white photographs),

acquired on 1956, average resolution of 32cm spatial resolution, after test reduce pixel size

process already done to obtain 10m resolution for both datasets (see Figure 2).

3. Data Pre-Processing

The analysis of multitemporal/multisensoral remote sensing data sets can only be efficiently

done if the data present itself in a common geometry. Geocoding of the images therefore has

to meet extremely strict requirements if the data obtained at different acquisition dates with

different systems are processed multitemporally in one "data stack". Geocoding of the

individual images of such a data set to the geometry of a topographic map is the most

common procedure to accomplish comparability. However, Due to the lack of GIS data and

digital elevation model, Ortho-rectification for the pair of images could not be achieved.

Image-to-image registration has been carried out so that an identical image coordinate system

could be established for assessment. The overall registration error with 160 reference points

was less than 2 pixels.

Figure 2: SPOT5 scene panchromatic 10m, 2005 (left)

and 10m B&W historical photograph, 1956 (right).

Figure 1: The Autonomous Community of

Catalonia, Spain.

4. Visual Interpretation

A visual interpretation of remote sensing data means not to overlay transparencies on images,

in the way, how conventional airborne photographs were interpreted in former days. In

present days the digital images are mapped by screen digitizing. In spite of the digital image

the interpretation technique is similar to the conventional airborne analogue photograph

interpretation. The advantage of computer aided visual interpretation with geocoded airborne

photographs (digital aerial maps) is the availability of rectified thematic maps. These can be

integrated into a GIS based system and can be combined with computer-aided classification

of settlement structures. The quality of visual interpretation is connected with the skills of the

interpreter. The more the interpreter knows about the landscape that is investigated the more

information will be generated. The general advantage of a conventional visual interpretation is

the high accuracy of the results. The pair of SPOT images and Arial Photographs was first

imported into ESRI ArcGIS 9.0 for digitizing the boundary of the artificial areas. After

completing image digitization, the GIS polygon layer representing the urban zones of the

above municipalities in 2005 was clipped by the one of 1956. The resulting GIS polygon layer

thus represents the change areas. The areas of these zones were calculated for assessing the

quality of the other image texture analysis

and classification approaches later. The

drawback of this methodology is the labour

intense and time-consumption, which may

take hours for the entire digitization process.

Figure 3 demonstrates the limits of visual

settlement structure interpretation with

conventional aerial map.

5. Working with texture analysis

Texture analysis can be categorized into structural level and statistical level textures and this

study would focus on the latter approach as it is more suitable for classification of natural

scenes, especially in satellite imagery [5]. In the statistical approach, the stochastic properties

of the spatial distribution of grey level (GL) in the image are characterized. The resultant

texture measures include statistics of grey level histograms, and autocorrelation and auto

regression models [6]. Amongst all popular algorithms, Grey Level Co-occurrence Matrix

(GLCM) is the widely adopted one [1]. First order and second order texture measures on

GLCM consists of Standard Deviation, Range, Minimum, Maximum and Mean. The second

Figure 3: digitized images for year 2005 (left) and 1956

(right).

Figure 4: To separate between the urban and non urban

areas in SPOT imagery (left) show clear results than

the Historical photographs (right) for the differentiation

in the Grey level in both areas

order of texture measures includes Angular Second Moment, Contrast, Correlation,

Dissimilarity, Entropy, Information Measures of Correlation, Inverse Difference Moment and

Sum of Squares Variance where majority of these could be found in commercial remote

sensing software package.

According to Marceau et al (1990), and Baraldi and Parmiggiani (1995), the window size for

texture analysis should be smaller than the smallest object to be mapped for easy

discrimination [7] [8]. As such, 5x5 window size was selected which was able to capture the

textural characteristics, especially those small isolated buildings despite of larger window size

(7x7, 9x9) for SPOT image for urban pattern study [5] [9].

Any increase of the window size in these studies was found not statistically significant.

Selection of GLCM texture is a critical factor affecting the classification accuracy.

Incorporation of excessive texture would degrade the performance and has been proven in

literature [5] [9]. Regarding the problem domain and the spectral information, incorporation

of texture measures were optimal in these researches. The remaining issue would be the

selection of appropriate texture. With the conclusion from Zhang et al (2003), Mean

combined with another GLCM texture feature

produced the best result amongst all

combinations of two GLCM texture. Squares

Variance could also provide the high

classification accuracy and for mathematical

morphology. Figure 4 shows the difference

results of Squares Variance over Spot

imagery and Historical Arial photographs.

6. Mathematical Morphology

In order to obtain continuous surfaces corresponding to built-up areas, an imclearborder has

been applied from binary images computed from the contrast texture analysis from above

result. Imclearborder suppresses structures that are lighter than their surroundings and that are

connected to the image border. The default connectivity is 8 for two dimensions.

Bwtraceboundary and bwboundaries already applied later to determined clear boundry could

including the urban areas, we already supposed the green areas such as Parks, Gardens.., etc

which already surrounded by urban areas could be green urban too and its included in the

built up areas. Bwtraceboundary traces the outline of an object in binary image. Nonzero

pixels belong to an object and 0 pixels constitute the background (see Figure 5).

Bwboundaries traces the exterior boundaries of objects, as well as boundaries of holes inside

these objects, in the binary image BW. bwboundaries also descends into the outermost objects

(parents) and traces their children (objects

completely enclosed by the parents). Figure 6

illustrates these components.

Mathematical Morphology (MM) is based on

the set theory combined with topological

notions (like continuity or limit of a phase).

The general principle is to compare the object

studied with an object of known form, named

structuring element [10] [11]. Several

algorithms of MM were explored in order to

extract built-up regions from a SPOT image

[12] As a MATLAB result, all the surfaces

which are not materialized by any pixel on the

marker image will disappear (see Figure 7).

7. Result and Discussion

We compared the results of these approaches based on visual quality and change detection.

Figures 8 shows extracts of the urban areas derived from texture analysis and mathematical

morphology approaches as aforementioned. The majority of the results match the geometry

and appearance of the flooding zones

derived from image interpretation.

Table 1 shows the result of the

computed urban areas derived from

image analysis methods with inclusion

of the two datasets: PAN image and historical photographs in Variance in texture channel.

GLCM and MM / Km2 Digitized areas / Km2

Spot 2005 80 km2 81 km2

photos 1956 47 km2 52 km2

2005-1956 33 km2 29 km2

Figure 5: Bwtraceboundary, P will specify the row and

column coordinates of the point on the object boundary

where tracing start.

Figure 6: Bwboundaries, The image must be a binary

image where nonzero pixels belong to an object and 0

pixels constitute the background

Figure 7: the damage between GLCM and MM five a

good urban detection for years 2005 (left) and 1956

(right).

Table 1: Urban Areas due to Barcelona municipality Discharge in

Km Square

1956 2005

A) GLCM and MM

1956 2005

B) Interpretation

2005-1956 2005-1956

C)Change Detection

Figure 8: Result of PAN and Historical Imagery

using a) GLCM and MM. b) Interpretation C) Change

detection.

The area delineated by visual interpretation

method is regarded as the reference for the

comparison. Table 1 shows also the difference

change detection in unite square comparing to

the visual interpretation result.

Total municipality area is 101 km2, the area

derived from GLCM and MM for Spot

panchromatic imagery is 80 Km square,

which is approximately similar as the

reference of the visual interpretation. In terms

of GLCM and MM for historical photographs

seems to be less than the reference 47 km2.

The difficulty encountered in obtaining such

results is due to fact that, at this scale, even

the exhaustive use of relevant iconic criteria

such as grey level texture, used to recognise

built-up areas, do not provide as robust results

as it could be obtained for the extraction of

cultivates areas, forest and hydrographic

networks. The entity of built-up objects

corresponds to a very large variety of

elements in the image. For example, accuracy

of the results is not so good when considering

small built-up areas on rural zones, where

construction may be more dispersed or where there is a too small contrast between buildings

and their surrounding. The results and the analysis reveal that: firstly, the visual image

interpretation approach are not efficient in terms of processing time. However, visual image

interpretation is the most accurate technique to compute the flooding areas. To meet the

requirement for the area computation of the flooding areas, it was found that GLCM and MM

produced the closest result to the visual interpretation. Although the accuracy of urban

sattelment is high enough to be accepted.

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Acknowledgements

The authors of this paper gratefully acknowledge the research funding provided by both the

European Commission through the ERDF, by way of the INTERREG IIIB Programme andthe

Spanish Ministry of Science and Technology (ref. BIA2003-07176). Thanks to the referees

for their help.