International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
• Production systems (Stilla, 1995; Stilla and Michaelsen,
1997)
• Blackboard systems (Nagao and Matsuyama, 1980)
• Fuzzy logic (Zadeh, 1987)
• Possibility theory (Dubois and Prade, 1985)
• Semantic nets (Quint, 1997a; Growe, 1999; Kunz, 1999)
The most common approaches are ruled-based systems and
semantic or Bayesian nets. During the last period, possibility
theory and fuzzy logic have been increasingly used. Semantic
networks in particular are attractive as they are flexible and
extendable and allow a complete modelling. Growe, 1999 gives
a nice example of their use, including extensions towards
multitemporal analysis. However, criticism has been expressed
that to built-up the network for each specific application is very
time-consuming and complicated, and thus such structures,
although theoretically and conceptually attractive, are not
suitable for implementation in commercial systems and use in
production practice.
Independently of the approach used, the above systems provide
very limited intelligence with respect to object recognition as
compared to humans. The major problems, apart from the
difficulty to transfer knowledge to a computer and encode it in a
computer-understandable form, are the following. The human
decision process is highly nonlinear. A single indicator may
lead us to change opinion radically and vote for an option that
had a small weight up to this point. Computers lack this ability
and their decisions are based on hard numbers (fuzzy logic
although it helps in combining information more "softly", still
does not solve this problem). Lack of learning and sensing
capabilities is another computer weakness. A final point, which
in our opinion is very decisive, is the lack of memory with
computer processing. For each individual task and application,
computers start with very little or no accumulated knowledge
and "experience". During the many years of development of
image analysis algorithms, the connection and relation to data
and knowledge bases has been almost totally ignored. The
integration of image analysis and GIS can thus also provide
benefits in this respect, as data storage and management systems
are integral GIS components. 6
6. SOME IMPORTANT POINTS
Before concluding, we would like to summarise here some
major points:
• Use of knowledge, rules, models
Although the last period the above information has been
increasingly used, it still is very weakly integrated in image
analysis of aerial and satellite imagery.
• Quality indicators
Reliable quality indicators of partial results are a must; they
should be provided as locally (fine-grained) as possible.
• Need of redundancy
Data fusion is often mentioned as a process to exploit
complementarities of the data. This is a correct statement.
However, data should also be combined just to provide
redundancy. This improves accuracy and especially reliability
and is one of the few counter-measures to account for the lack
of algorithm intelligence mentioned above.
• Reliability
The lack of reliability in the results is the single, most crucial
factor, which hinders an increased use and efficiency of
automated procedures.
• Need of extensive tests
Published results, especially from academia, refer to very
limited datasets, which are used for years, and after careful
tuning of the algorithm parameters in order to get good
results. Use of extensive, comprehensive datasets in
cooperation with the industry is necessary. This will provide
not only a framework for an objective comparison of methods
but also provide useful feedback and possibilities of
improvement of the used algorithms, strategies and input data.
• System architecture and strategy
To account for various scenes, objects, object types and
applications, without having to redevelop for each case
everything from scratch, the whole strategy, architecture and
control of the image analysis system should be modular,
flexible and adjustable.
• Quality of algorithms
The quality of the individual processing algorithms (e.g. for
edge extraction, 3D matching etc.), although probably not the
most important factor in achieving high quality results, can
make a difference and should be chosen accordingly.
• Selection of input data and preprocessing
This aspect can make a huge difference in performance. While
development of sophisticated algorithms with poor input data
might cost a lot and bring little, more suitable data, permitting
an easier detection of the target object and its discrimination
from other ones, might lead to significant improvement of the
results, even when using simpler processing algorithms. The
fact that an increasing number of sensors become available
should be exploited in this direction. The criteria of data
selection should serve the better separability of the target
object, provision of data complementarity and redundancy,
while correlations between the data should also be modelled
and taken into account.
• Importance of data structures and use of databases
As mentioned above, the system should have an active and
continuously increasing memory through accumulation of
knowledge and data. To store and manage this information,
appropriate data structures are needed, which are
unfortunately not provided yet by commercial systems. Such
data structures should be able to include heterogeneous and
multivariate data (multi-valued, raster, vector, attribute etc.),
support flexible data types, include 3D and temporal
modelling. An object-oriented paradigm with inheritance,
encapsulation, methods associated with the objects etc. could
be useful in building up such data structures.
• Quality control of final results and feedback
Quality control is naturally essential but apart from that it
should be used for the analysis of the size of the occurring
errors, where they occur and why. The results should also be
used to derive statistical data on the extracted objects and
their attributes, which can be accumulated in a "ground truth"
database and used in future similar processing tasks, e.g. for
