forest receives a more precise (specialised) definition and associated behaviour from the underlying data model than 
does the unlabelled region in the original image. 
Feature formation is often closely coupled to abstraction-, the newly-formed features are described in simple terms 
within the data model, with internal complexity hidden from the user by structural abstraction. Also closely coupled 
to the notion of extraction is that of in(tro)duced error or uncertainty. As the definition of a region in space is made 
more precise (certain), the confidence with which the definition applies must decrease. This is due to the 
generalisation that has been applied to the data in order to perform extraction. It is interesting to note that the 
opposite is often implied: that is, the definition is precise therefore the accuracy must be high. The amount of 
uncertainty that is introduced is determined by the degree to which generalisation is required. For example in 
landuse classification, highly homogeneous and well-defined regions (such as paddocks) will not incur the same 
uncertainty overhead as poorly defined heterogeneous regions (such as remnant vegetation). 
2.4.2 Multiple Spatial Representations 
The description of a region as a feature of interest should be regarded as only one element in a set of possible 
descriptions that could be formed of the feature, for example by changing the data source, or the algorithms and 
control parameters used to interpret the data. The full range of possible interpretations of a feature may offer some 
insight into the uncertainty inherent in the extraction process, since the effects of applying different classification 
strategies and object formation methods may be analysed and compared. 
Existing GIS are built on the false premise that a feature can be precisely and unambiguously defined by some 
process, and this definition then placed in a data store. Unfortunately, such an approach is a gross simplification of 
the real situation. The idea that a single feature can be accurately described by a single spatial representation is often 
inappropriate and indeed can be very limiting, for any of three possible reasons outlined below. 
(i) The science of image processing does not produce exact results and it is often advantageous to include more 
than one interpretation of a scene fragment within the database. That which defines a feature as distinct from its 
surroundings is often unclear and, in some cases, a single interpretation of a scene upon which experts would agree 
is often not realisable 4 . Indeed, it would seem that the task for which a feature is to be used has a great influence on 
how that feature might best be described spatially. 
(ii) A variety of remote sensing platforms can provide source data, each one having different sensing capabilities, 
including spectral response and spatial resolutions. So, the representation that might be constructed will vary 
according to the source of the image data, giving rise to problems related to cartographic generalisation (Muller, 
1991), in that different representations of the same feature can be derived from data of different resolutions. 
(iii) Geographic data is by nature dynamic. Image data is collected on a regular basis over the same location (every 
16 days for LANDS AT TM, 2.5 days for SPOT). This poses the immediate problem that several 'versions' of each 
recognised geographic feature may be created even if the sensor and feature extraction policy remain fixed. If the 
effects of temporal change are to be studied it becomes necessary to keep these versions as time-separated 
representations of the same feature (e.g. Worboys, 1992: Langran, 1992). 
In summary, the representation of a feature is affected by: (i) the feature extraction techniques used; (ii) the platform 
or source of data used and (iii) the time from which the data is drawn. 
2.4.3 Distinction of Thematic Data and Object-Classes 
An important distinction must be drawn between classes defined by data classification procedures and classes as 
defined by the object data model. In the object model, a class is used to describe a set of objects of the same logical 
type, and is composed of two parts. The intension is the definition of a distinct type of object, the extension is the 
4 For example, if there are n geologists, there are at least n +1 opinions as to how the data should be interpreted.