Characterising Elements of Urban Morphology From
High Resolution Optical Remote Sensing Images
URBAN MORPHOLOGY AND REMOTE SENSING
Research into ways of characterising or inferring elements of urban morphology by image analysis
of high spatial resolution images from earth-observing satellites shows promise (Mesev, 1998;
Mesev et al. 1995; Barnsley and Barr, 1996). The continual advances in remote sensing and
computing technologies mean that image analyses of high (< 5 m) and very high (< 1 m) spatial
resolution images now facilitate discrimination of ever finer elements of the urban fabric to give
new representations of urban form and spatial structures (Donnay et al. 2001). The focus of current
research is on the extent to which one may characterise or infer elements of urban morphology by
image analysis and on the value of the resulting data for populating integrated geographical
information systems of cities (Mesev et al. 1995; Longley and Mesev, 2001; Batty 1999).
Urban morphology developed as a branch of urban geography concerned with relationships
between urban form, urban function and change (Webster, 1998). With developments in
mathematics and computation in the 1970s and 1980s, urban morphology grew to emerge today as
an inter-disciplinary research field with a focus on urban systems and urban dynamics, descriptions
of urban form and of ways of classifying spatial and temporal relations (Forster, 1983; Batty, 1999).
Although cities are socio-economic systems with flows of people, goods and services, the built
environment is typically an artifact of past and contemporary urban processes (Longley and Mesev,
2001). Measurement of urban form only provides a static picture, based on measuring at one cross
section in time, of urban morphology. There are however many urban morphologies apart from
those based solely on urban fabric (Martin, 1991). These relate for example to the environment, to
population, employment, industry, health and other socio-economic variables and to spatial and
temporal relations between these and urban form (Weber and Hirsch, 1991; Wood et al. 1999;
Martin, 1991). The goal in studying different morphologies is to develop a deeper, more consistent
and more up-to-date understanding of urban processes and thereby inform urban planning.
Although information derived from image analysis of remote sensing images is only likely to
contribute a small part of the total information that is required, it can potentially be a significant
part which gives a synoptic, consistent and instantaneous view at one or more scales. By
combining sequences of such views, changes to urban form can be detected. In this way, remote
sensing can contribute to research in urban geography and planning. Of course, there are many
critical challenges and obstacles that need to be overcome before we succeed in improving the
quality of life of people living in cities. The hope is that the combination of remote sensing and
integrated geographical information systems for analysis, modelling, research and planning of
cities can contribute towards this end.
There is a significant gap between the potential value that satellite remote sensing and image
processing technologies can bring and the actual value being realised today. New very high spatial
resolution images from optical remote sensing systems on earth observing satellites provide the
essential first step in narrowing this gap. This is because scale-space relations are critical in urban
remote sensing and the spatial resolution of images is the single most important technical issue in
urban remote sensing (Welch 1982). The second step will be to devise improved theories and
methods of image analysis to characterise and extract elements of the urban morphology from very
high spatial resolution images (Ebner et al. 1999; Heipke et al. 2000). Although image analysis of
high spatial resolution images generally allows improved discrimination of urban features, the
effects of relief displacement, shadows from tall buildings and other distortions cause significant
difficulties in very high spatial resolution images of metropolitan areas with high rise buildings or
steep relief.
The aim of this paper is to investigate methods of extracting linear features from dense urban areas
in cities using edge and line detection methods. Evaluation of the potential of certain techniques (to
be described below) for delineating road networks from Indian Remote Sensing satellite (IRS) and
IKONOS panchromatic images is of particular interest.
