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olutions. By 
dge, one hopes 
licability to 
ystems can be 
used to plan the acquisition and processing of 
remotely sensed data. It is not uncommon for 
tracking stations to be acquiring data from several 
satellites, some of which have complex configur 
ations and control. Expert systems can contribute 
also to the analysis of remote sensing images, as 
discussed in section 5.2, and to the integration of 
geographic information. For specific application 
areas, such as crop or forest inventories, expert 
systems can be effective in guiding inexperienced 
users through complex resource management 
procedures. 
CCRS has been conducting research and development 
in artificial intelligence in order to create a 
novice advisor, the Analyst Advisor, described in 
section 5.2 and an expert system for comparing two 
maps and maps and images (section 5.3). This devel 
opment has taken place on DEC AI VAXstations. With 
a large investment in FORTRAN code on three other 
computers, we chose to retain FORTRAN for numeric 
processing and to use Prolog for symbolic reasoning. 
Figure 9 shows schematically the general architec 
ture of a GIS/lAS combination with an expert system. 
Since often the GIS is a commercial system for which 
the user has little access to the software inter 
nals, we show the expert system more intimately 
connected to the image analysis system. The user's 
knowledge, in the form of facts, rules, and heuris 
tics, is used by the expert system to produce 
advice, and control, of the IAS. 
5.2 The Analyst Advisor 
The Analyst Advisor is a hierarchical expert system 
(Figure 10) . Such a structure was selected because 
there was a large variety of expertise and image 
processing techniques to be applied to a particular 
application. This approach is highly structured and 
modular. There are a number of levels of authority 
organized in a pyramidal structure. The highest 
level sets broad goals for the next level of com 
mand. The lowest level corresponds to the image 
processing algorithms, the FORTRAN programs. With 
this hierarchy, there are well-defined lines of 
communication and a limited span of command. 
The top level expert (Figure 10) is also referred 
to as the Analyst Advisor. It guides the user 
through an analysis session. To simplify our 
development, we have focused on the application of 
updating forest inventory geographic information 
systems. In several boxes of Figure 10, we identify 
the expert system by name and include reference to 
the LDIAS Task Interface (LTl). The structure of 
our FORTRAN software with its unique man-machine 
interface enables our expert systems to control the 
image processing software. 
the earth, altitude above some geoid model surface, 
and so forth. These values for a slot can be 
obtained by a computational procedure (demon). 
Rules are in the form of production rules ("if 
CONDITION then CONCLUSION"). There are two control 
strategies that can be followed: backward-chaining, 
and forward-chaining. 
The Analyst Advisor runs on an AI VAXstation and 
controls a user workstation. Since the components 
of this workstation include image and map displays 
attached to different computers, the Analyst Advisor 
must control processes across a network of compu 
ters. For the grouping of training areas and clas 
ses we have made use of evidential reasoning with 
uncertainty values derived from the confusion 
matrices. In an environment such as ours where one 
is analyzing complete thematic mapper scenes, one 
must be able to pause the hierarchical expert system 
and later resume an analysis session. This problem 
has been solved. One can obtain explanations from 
several expert systems. The second prototype of the 
Analyst Advisor is scheduled to be released to our 
users in March, 1987. Experimentation with the 
Analyst Advisor will lead to the inclusion of more 
knowledge for forestry applications. In the next 
year, the MICE expert system described in the next 
section will be integrated with the Analyst 
Advisor. 
5.3 The MICE Knowledge-Based System 
As has been shown, there must be a correct geometric 
correspondence or registration between the digital 
maps (or geographic information system levels) and 
the images. This correspondence criterion also 
applies to the maps used as a base for geometric 
correction of the remote sensing imagery. The pres 
ence of non-systematic errors, as shown in section 
4, necessitates that one use symbolic reasoning to 
identify and reason about feature mis-matches. The 
Map/image Congruency Evaluation (MICE) knowledge- 
based system is designed to determine the spatial 
differences or similarities of maps or maps and 
images. The MICE expert system is described in 
detail in Plunkett et al (1986). 
The architecture of the MICE expert system is 
shown in Figure 11. It makes use of the same shell 
(inference engine) as the Analyst Advisor. Certain 
classes are better for congruency evaluation than 
others. Table 6 lists the classes in order of rank 
(1 is highest) for matching. The best class is the 
hydrography class, which includes rivers, lakes, 
shorelines, and creeks. 
Table 6. Best classes for congruency evaluation 
For each expert we use the same Prolog shell, our 
remote sensing shell, RESHELL. A more complete 
description of the Analyst Advisor and RESHELL 
appear in Goodenough et al (1987) and Goldberg et al 
(1985). RESHELL uses a blackboard structure for 
storing known and deduced information. On this 
blackboard are stored the agenda created by the 
meta-rule interpreter, goals from a higher level 
expert, and object values. The shell includes an 
explanation facility, a scheduler (inference en 
gine), a knowledge editor, and a frames database. 
The frames database contains more static and global 
information and stores these in a manner that pre 
serves descriptive, semantic relationships between 
objects. For example, a frame called LANDSAT-5 
would have attributes (slots) for altitude, acqui 
sition frequency, sensors, etc. The value that one 
would retrieve from a slot would depend upon the 
perspective being used. Thus, one might retrieve 
for altitude, for example, altitude directly above 
Class Code 
Class Description MICE Rank 
G 
D 
E 
I 
H 
C 
B 
A 
F 
J 
Hydrography 
1 
Highest 
Road and Rail 
2 
Utility 
3 
Land Cover 
4 
Hypsography 
5 
Structures 
6 
Buildings 
7 
Designated Areas 
8 
Delimiters 
9 
Text 
10 
Lowest 
The MICE expert system follows the same strategy 
as a human; namely, 1) preprocesses the map and the 
image to the same spatial datum and symbolic repre 
sentation; 2) locates corresponding segments; 3) 
reports on the spatial congruency of the corres 
ponding segments or structures. Presently,