Image Processing Reference
In-Depth Information
and spectral image information with contextual information, is therefore often
preferred. Even so, the identification and delineation of homogeneous land use
units is subjective. Further, the position of the unit boundaries and the nature of
the mixed uses within each unit are difficult to precisely define or describe. These
problems are particularly apparent when a group of interpreters works on the same
area or when multi-temporal data extraction is performed.
Mapping the morphology of urban areas, which intuitively appears to be a rather
straightforward process, entails complex technical issues related to the state of
development at a given location and time. For example, new urban areas where ser-
vicing and building are occurring (see Fig. 5.2 ) tend to have a high spatial and
spectral variance due to the great variety of land cover types within relatively small
areas. Nevertheless, several authors emphasize the usefulness of remote sensing data
for detecting and measuring elements related to urban morphology (Mesev et al.
1995 ; Webster 1995 ; Yeh and Li 2001 ). Yeh and Li ( 2001 ) used the concept of
entropy to analyze urban sprawl and different
growth patterns in the Pearl River Delta, China.
Entropy is a measure of disorder within a certain
system and has been used by Yeh and Li ( 2001 ) to
measure the degree of spatial concentration or dis-
persion of urban sprawl. Low entropy relates to
concentrated development while high values indi-
cate more scattered development patterns.
Since the availability of high spatial resolution data (<5 m), feature recognition-
and object-based approaches for data extraction (application examples of these
approaches are given in Chapter 10) are becoming increasingly important in
urban applications. A basic consideration in these approaches is the ability to rec-
ognize and demarcate discrete/individual objects (Laurini and Thompson 1996 ).
An object with a discrete spatial extent such as a building can be detected and
demarcated pending on the spatial resolution of the image (e.g., a large building
of 50 × 50 m can be delineated in an image of 5 m spatial resolution or smaller,
whereby the accuracy and precision of the delineation improves with increasing
spatial resolution). However, the ability to detect and demarcate an object is also
affected by other properties of the object. For example, a building with highly
reflective roof material such as corrugated galvanized iron sheets, may be difficult
to detect if the surrounding environment of the building is bare sand.
The most recent techniques for object extraction and classification typically
use spectral information from individual pixels in conjunction with information
on the texture, shape, color and/or height properties of
the objects of interest (Thurston 2002 ). A multi-reso-
lution, multi-sensor approach using such characteris-
tics is a feature of some recent work. For example,
Ehlers (2002) uses a hierarchical approach that com-
bines existing GIS data with elevation data and multi-
spectral imagery, obtained simultaneously from the
TopoSys II system, to develop methods for object
low entropy value relate
to concentrated develop-
ment while high values
indicate more scattered
development patterns
feature recognition
and object-based
approaches are
becoming increas-
ingly important in
urban remote sens-
ing applications
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