In the last few decades, Urban sprawl refers to the outgrowth of urban areas ... photographs and SPOT imagery for monitoring urban land use change; and (2) ...
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 georeferencing 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 crossborder 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 northeast 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).
Figure 1: The Autonomous Community of Catalonia, Spain.
Figure 2: SPOT5 scene panchromatic 10m, 2005 (left) and 10m B&W historical photograph, 1956 (right).
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.
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
Figure 3: digitized images for year 2005 (left) and 1956 (right).
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
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.
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
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).
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
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).
Figure 7: the damage between GLCM and MM five a good urban detection for years 2005 (left) and 1956 (right).
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 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
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
Table 1: Urban Areas due to Barcelona municipality Discharge in Km Square
of the two datasets: PAN image and historical photographs in Variance in texture channel.
The area delineated by visual interpretation
A) GLCM and MM
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
1956
2005
panchromatic imagery is 80 Km square, B) Interpretation
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 1956
the exhaustive use of relevant iconic criteria
2005
C)Change Detection
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 2005-1956
elements in the image. For example, accuracy of the results is not so good when considering
2005-1956
Figure 8: Result of PAN and Historical Imagery using a) GLCM and MM. b) Interpretation C) Change detection.
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.
References [1] Haralick, R. M., Shanmugan, K., and Dinstein, I., 1973. “Textural Features for Image Classification”. IEEE Transactions on Systems, Man and Cybernetics, 3 (6), 610-621. [2] Marceau, D.J.; Howarth, P.J.; Dubois, J.M.; Gratton, D.J. 1990. “Evaluation of the greylevel co-occurrence matrix method for land-cover classification using SPOT imagery”. IEEE Transactions on Geoscience and Remote Sensing, 28: 513-519. [3] Haralick 1979. Haralick, R.M., 1979. Statistical and structural approaches to texture. Proc. IEEE 67 5, pp. 786–804 [4] Campaign against the Quart Cinturó of Barcelona, Land Use Planning and Transformation of Space, the Barcelona fourth ring road project, Sabadell, Barcelona, Spain. http://ccqc.pangea.org/eng/ocuterri/tereng.htm (accessed 1991) [5] Shaban, M.A., and Dikshit, O., 2001. “Improvement of Classification in Urban areas by the Use of Textural Features the Case Study of Lucknow City”, Uttar Pradesh. International Journal of Remote Sensing, 22(4), pp. 565-593. [6] Narasimha Rao, P.V., Sesha Sai, M.V.R., Sreenivas, K., Krishna Rao, M.V., Rao, B.R.M., Dwivedi, R.S., and Venkataratnam, L., 2002. “Textural Analysis of IRS 1D Panchromatic Data for Land Cover Classification”. International Journal of Remote Sensing, 23(17), pp. 3327- 3345. [7] Marceau, D.J., Howarth, P.J., Dubois, J-M. M. and Gratton, D.J. 1990. “Evaluation of the Grey-Level Co-Occurrence Matrix Method for Land Cover Classification using SPOT Imagery”. IEEE Transactions Geoscience and Remote Sensing, 28(4), pp. 513-519. [8] Baraldi, A., and Parmiggiani, F., 1995. “An Investigation of the Textural Characteristics Associated with Gray Level Cooccurrence Matrix Statistical Parameters”. IEEE Transactions on Geoscience and Remote Sensing, 33(2), pp. 293-304. [9] Zhang, Q., Wang, J., Gong, P., and Shi, P., 2003. “Study of Urban Spatial Patterns from SPOT Panchromatic Imagery using Textural Analysis. International Journal of Remote Sensing”, 24(21), pp. 4137-4160. [10] Coster and Chermant, 1989. M. Coster and J.L. Chermant, “Précis d'Analyse d'Images”. In: , C.N.R.S.,, Paris (1989), p. 560. [11] Serra, 1982. J. Serra, “Image Analysis and Mathematical Morphology”. In: , Academic Press, New York (1982), p. 610. [12] DAVIE, M.F. and DROUOT, J-L., 2000, “La périphérie urbaine et les extensions de la ville de Beyrouth (Liban): étude par traitement d'une image SPOT”, Cybergeo : Revue Européenne de Géographie, No.157, 25/04/00. (http://www.cybergeo.presse.fr).
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.
