Image Processing Reference
In-Depth Information
Table 5.1 Land use/cover classiication levels (Anderson et al. 1976 )
Resolution (m)
Example of class
Built-up urban
Residential, industrial, commercial etc.
Single family units, apartments, etc.
Additional information e.g. condition of
the building
recent work of Jensen and Cowen ( 1999 ) incorporates other aspects and categories,
including hierarchical object classes.
In order to give a visual impression of how the spatial resolution of a sensor
influences object and land use identification in urban areas, several examples of
different urban land uses and sensors for the city of Enschede, The Netherlands, are
shown in Fig. 5.3 and discussed in Table 5.2 . The examples shown in Fig. 5.3 follow
a similar work done by Radnaabazar et al. ( 2004 ) for Ulaanbaatar, Mongolia.
Clearly, for identifying small urban objects or objects in a complex environ-
ment, very high resolution data is a prerequisite. Data of 10 or 15 m spatial reso-
lution may provide an overview of urban areas and general land cover/use classes.
However, object recognition requires a minimum of 5 m resolution or less, in
addition to any case-specific consideration of other characteristics such as culture
or morphology.
The diversity of urban morphology becomes apparent when comparing the for-
mal urban development of Enschede (Fig. 5.3 ) with various types of urban develop-
ment found in Dhaka, Cairo, and Dar es Salaam (Fig. 5.4 ). Informal areas in Dar es
Salaam typically consist of single-story buildings that were constructed in an incre-
mental and haphazard manner. On the other hand, many informal areas in Cairo
follow the regular pattern of former agricultural fields and contain buildings that are
densely packed and frequently exceed 5 floors in
height, resulting in extensive shadows (see, for
example, the prominent shadows cast by buildings
in the 100 × 100 m window). In order to distinguish
individual buildings in such cities, very high-resolu-
tion images are necessary. This is demonstrated by
the examples in Fig. 5.4 , which shows images rang-
ing from a spatial resolution of 30 m (LANDSAT ETM+) to 20 cm (SFAP).
In practice, the selection of a particular data source is a compromise between
costs, required spatial resolution, date of the image, other image characteristics
such as the number of bands, and data availability (Harris and Ventura 1995 ). The
accuracy of a classification (e.g., a land use classification) is highly dependant upon
the selected spatial resolution (Welch 1982 ). The desired accuracy and the required
information are therefore valid criteria for the selection of sensor data with an
appropriate spatial resolution (Atkinson and Curran 1997 ).
The spatial resolution required for a given study could be determined by the size of
the smallest target objects (see, for example, Forster 1985 ; Cowen and Jensen 1998 ).
object recognition
requires very high
spatial resolution
imagery (minimum of
5 m resolution or less)
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