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Modularity allows easy expansion of the system, 
because the knowledge base of a modular system 
can be changed without altering the structure of the 
main program. 
In recent years, personal computers have become 
powerful enough for image processing and GIS data 
base management. EOSAT (1988), for example, 
makes TM floppy disk images covering 512 x 512 
pixels commercially available to interested users. 
Developing the system for use with 
microcomputers, or allowing portability between 
mainframe and microcomputers, is therefore 
important to avoid redundancy in programming. 
User and system design requirements are not 
necessarily independent from each other and 
meeting some requirements is more important 
than others. For example, efficiency in terms of 
time and cost was also selected as an important 
requirement but has, in the initial design phase of 
the system, a lower priority than accuracy of feature 
identification. 
4 IMPLEMENTATION 
4.1 Study Area 
A quadrant of a TM image centred east of Calgary 
Alberta, Canada and acquired in late July, 1985 
was chosen as the study area. This region, 
predominantly agricultural landuse, is typical of a 
North American prairie landscape. It is suitable as 
a test area, because it consists of a fairly uniform 
low variance data set. However, it shows a variety 
of features commonly placed on 1:50,000 maps, such 
as roads, urban areas, railways, fields, and 
hydrographic features. The performance of the 
system in identifying these features can therefore be 
tested without interference from noise or highly 
variable terrain. 
4.2 Image Analysis 
Before this image can be input to the interpretation 
system, it has to be smoothed to remove excess 
variance, and segmented into its characteristic 
regions. Working with regions rather than 
individual pixels during the interpretation is easier, 
because it corresponds to the units the human eye 
recognizes. In addition, the data volume the 
system has to work with can be reduced (Perkins et 
al., 1985). 
All image bands except for the thermal band of the 
256 x 256 image window of the study area were 
enhanced using principal components analysis. 
The three dominant principal components 
accounted for 97.8 percent of the variation within 
the scene, the first component (70.85 percent) 
representing scene brightness, the second 
component (17.28 percent) representing biomass, 
and the third component (9.67 percent) 
representing 'greenness' in the image. These three 
components were used as input to a migrating 
means clustering algorithm for image 
segmentation. Twelve distinct clusters were 
extracted from the imagery. Of these, one cluster 
was found to represent water, four clusters various 
non-vegetated surfaces and the remaining seven 
vegetated surfaces. 
A comparison with a manual segmentation of the 
study area indicated that the machine segmentation 
was of acceptable quality. Discrepancies occurred 
because of the machine's better ability to 
distinguish subtle spectral differences, e.g. due to 
different moisture conditions within a feature. In 
these situations, rather than representing such a 
feature as a single logical unit within the image 
database, it would be broken up into distinct 
polygons with slightly different spectral properties. 
One objective during testing of the image 
understanding system therefore was to determine 
if, and how well, the system could resolve such 
conflicts. 
4.3 Image Understanding 
This part of the system was designed as a prototype 
which should eventually be expandable for a wide 
variety of image interpretation tasks. In order to 
keep the problem domain (and therefore the 
knowledge base) manageable in the initial design 
phase, some simplifying assumptions were made: 
(a) The system is aimed at a North American 
prairie landscape. 
(b) Only features recognizable on TM imagery 
(green, red, infrared bands) were considered. 
No height information is included. 
(c) Some attributes of features (e.g. pattern) are 
user supplied and not automatically extracted. 
(d) Rules are aimed at imagery acquired during 
the growing season. 
The types of queries the system must be capable of 
answering (see Section 3.2) require a flexible data 
structure, which can incorporate values for all 
attributes of interest (see Table 1). The system 
should also be expandable for more complex 
situations. This type of information can best be 
encoded in a production rule environment. Such 
rules are conditional statements which are easily 
modifiable, because they are relatively independent 
from each other (Bratko, 1987). The rules were 
determined by visually interpreting the TM image 
of the study area and by studying what kind of 
attributes are most commonly associated with 
individual features. Some textual examples of 
rules encoded in the knowledge base are: 
Rule: If the feature is circular and shows high 
reflectance in the infrared region of the 
E-M spectrum, then the feature is a pivot 
irrigation field. 
Rule: If the feature is linear, shows high 
reflection in the green and red and forms 
a dendritic pattern, then the feature is a 
river.