bodies, and rangeland. It was found that the most 
easily identifiable features were those that are by 
definition regular in shape and/or spectrally very 
homogeneous - in other words fields, water bodies, 
and man-made structures. Fallow fields, which are 
both homogeneous, spectrally speaking, and regular 
in shape, were correctly identified in nearly all 
cases, and were successfully distinguished from 
other non-vegetated surfaces. Planted fields, which 
often show subtle variations in their spectral 
responses, resulting in several distinct polygons 
making up one field, could often be successfully 
identified by using spatial relations, such as 
adjacency. This made it possible to determine 
whether contiguous polygons of similar spectral 
response could be updated into one homogeneous 
feature, matching conditions in the knowledge base 
required to label it as a field. 
However, the system cannot at the moment 
successfully extract those features which are neither 
regular in shape nor exhibit homogeneity in their 
spectral response. An example of such a feature is 
rangeland, or vegetated areas in general, that are 
not cultivated. What the system can do, is 
tentatively label individual polygons in the image 
database as rangeland, but it is not successful in 
using adjacency and other spatial relations to extract 
entire units. The implication of this is that, for 
more complex features, e.g. units of terrain, the 
simple spatial and spectral measures currently used 
in the system are not sufficient for identification 
and will have to be supplemented by ancillary 
information, such as topography and soils. It also 
implies that when designing a knowledge base for 
more complex applications, such as terrain analysis, 
ancillary information, e.g. in the form of a digital 
elevation model, will be necessary for the system to 
perform satisfactorily. 
Since the structure of the system as it currently 
stands is quite modular, experimenting with 
different types of ancillary information, and with 
different combinations of spatial and spectral 
attributes, to improve the scope and the robustness 
of the current system, should be a relatively straight 
forward matter. Experimentation with additional 
attributes would ideally include different 
combinations of spatial and spectral attributes, 
more complex shape measures, and more complex 
descriptors of spatial and spectral knowledge in the 
knowledge base itself. 
6 CONCLUSIONS 
Using a rule based system for automated feature 
extraction is attractive, because of the ability of these 
systems to structure information from diverse 
sources flexibly and in a modular fashion. The 
prototype system introduced in this research takes 
advantage of these properties by using a variety of 
spatial and spectral information extracted from 
images to identify rural landuse features in Landsat 
TM imagery. 
The system exhibits success in extracting simple 
landuse features by using only image-derived 
spatial and spectral attributes. However for more 
complex applications, such as terrain evaluation, 
the use of ancillary information, as well as more 
complex spatial attributes than those currently 
employed, will be a necessity for successful 
operation. In addition, a fully operational system 
should provide the capability to fully integrate both 
algorithmic and expert knowledge under one 
control structure to facilitate any image 
manipulation functions or numerically intensive 
computations that might be required for the 
extraction of more complex spatial features during 
the image interpretation process. 
While the system described here is able to identify 
features from digital imagery, the interpreted 
images are still a long way from being directly 
integratable into a GIS. The main reason for this is 
that no uncertainty model has been developed to 
indicate the degree of confidence associated with an 
interpretation, especially where a choice exists 
between several alternatives. This type of 
ambiguity could be dealt with by incorporating a 
fuzzy measure within the system (Klir and Folger, 
1988). 
If the automated image interpretation system is to 
become a true expert system, then the development 
of an explanation facility and a natural language 
user interface is a necessity. Such an interface could 
deal with ambiguity in attribute values (Karimi and 
Lodwick, 1987). The consistency of interpretations 
could therefore be improved for those attribute 
values which are user supplied. Finally, the 
development of an explanation facility, especially 
where it supplies answers about unexpected results, 
could serve as a learning tool for an inexperienced 
image analyst. 
7 REFERENCES 
Avery, T.E. and Berlin, G.L., 1985. Interpretation of 
aerial photographs. Burgess Publishing 
Company, Minnesota, 554 pp. 
Bratko, I., 1987. Prolog programming for artificial 
intelligence. Addison Wesley Publishing 
Company, Reading, Massachusetts, 423 pp. 
Burrough, P.A., 1986. Principles of geographical 
information systems for land resources 
assessment. Clarendon Press, Oxford, 193 pp. 
EOSAT, 1988. Landsat data user notes. Earth 
Observation Satellite Company, Lanham, 
Maryland, Vol. 3, No. 1. 
Estes, J.E., 1977. A perspective on the state of the art 
of photographic interpretation. Proceedings of 
Eleventh Symposium on Remote Sensing of 
Environment, Ann Arbor, Michigan, October 
1977, pp. 161-176. 
Estes, J.E., Sailer, C. and Tinney, L.R., 1986. 
Applications of artificial intelligence techniques 
to remote sensing. The Professional 
Geographer, Vol. 38, No. 2, pp. 133-141. 
Genesereth, M.R. and Ginsberg, M.L., 1985. Logic 
programming. Communications of the ACM, 
Vol. 28, No. 9, pp. 933-941. 
Goodenough, D.G., Goldberg, M., Plunkett, G. and 
Zelek, J., 1987. An expert system for remote 
166