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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
3. KNOWLEDGE BASED EXPERT SYSTEMS 
STRATEGIES 
Two fundamental extreme types of inference mechanisms are 
utilised in Artificial Intelligence : 
- Domain Dependent Inference (DPI): is suitable for 
relatively explicit body of evidence in which the 
conclusion is entailed and no, or low level of inference 
is required. DDI inference mainly controls the 
combination order of the different evidences and is 
represented in the procedural knowledge. The 
sophistication in expert systems based on DDI is 
embedded within them during their rules’ formation 
rather than in their functioning way. The construction of 
procedural knowledge and evidential basis requires 
heavy information analysis, learning procedures and 
feature assessment. Rule based expert systems 
implementing binary decision trees are extreme 
examples of DDI and are widely used in remote sensing 
(e.g., Goodenough et al.,1994; Kartikeyan et. Al.,1995; 
Chan et al.,1999). GeoAIDA (Tonjes et al.,1999) is a 
good example of such strategy, where expert system 
based on semantic networks was developed 
implementing specific (explicit) evidence, including 
contextual information. The inference mechanism 
applied was then a procedural sequential decision tree 
type. Other extremely different example of this strategy 
concerns the use of Classification Tree Analysis (CTA) 
techniques and their recent improvements with Boosting 
and Bagging methods (e.g., Lawrence et. al.,2004). 
Again in this method there is maximal exploitation of 
the information content in the data and the 
(computerized) construction of a decision tree 
specifically (explicitly) applicable to the data at hand. 
- Domain Independent Inference (DII): is suitable for 
relatively implicit body of evidence. The DII inference 
relate to their associated characteristics: the relative 
belief, support, certainty and weight, rather the 
information sources themselves and thus is independent 
of any specific recognition or decision making problem. 
The combination of evidence would then be based on 
general deductive, inductive or abductive procedures 
(Durkin, 1994). A significant element of the intelligence 
represented by DII is embedded within it during the 
development of the generalized inference algorithm 
which is independent of any specific recognition or 
decision making problem, and usually adopted without 
investing in such development. The sophistication of the 
system which is embedded in the inference 
methodology is then dependent on the inference 
capabilities to overcome non-uniqueness and conflicts 
(e.g., Cohen and Shoshany,2005). 
In reality there is rather a mixture of strategies tailored 
according to information sources and expert systems tools 
available. However, there is relatively little comparison made 
between DDI and DII based strategies in terms of their 
performance and the types of evidences used. Performance 
assessment of these strategies in areas which differ from the 
training areas is of special interest. Would it be better in terms 
of 'cost' and performance to deepen the search for domain 
explicit evidence or to broaden the implicit evidential basis? 
These same questions are addressed in "Information Foraging". 
4. THE INFORMATION FORAGING PERSPECTIVE 
Chamov,(1976) and Stephens and Kerbs,(1995) proposed the 
foraging theory to explain foraging behavior and strategies in 
nature. This theory is based on the observation that animals 
evaluate the availability of food sources and at the same time 
the efforts required in order to consume them. Prey searching 
efforts, competition with other predators and energy required to 
catch the prey are among the main ' costs’ of obtaining the 
food. The amount of food (prey size) gained and its quality 
represents the gain from these efforts. 'Within Patch' searching 
strategies concern exploiting the food available at a certain area 
before migrating to another patch. While such strategy 
minimizes the search and migration energy, there is decrease in 
food availability and maybe increase competition. In the remote 
sensing context such strategy would be represented by limiting 
the information search for one data source: visible, IR or 
microwave spectral bands. Domain Dependent Inference (DDI) 
strategies are also a type of 'Within Patch' foraging behavior. 
'Between Patch’ strategies represent large area search for 
patches of high food availability or low competition. Its 
implementation requires gathering of larger amounts of data 
and better skills in optimizing their use. Animals adopting 
these strategies will have higher resilience to changing 
conditions and would reduce the potential degradation from 
overgrazing. In the remote sensing context 'Between Patch' 
strategies would be characterized by implementing multiple 
sources (e.g., multi-spectral; multi-temporal; multi-resolution). 
The uses of Domain Independent Inference (DII) strategies are 
analogous to this later animal behavior. 
'Translating' the foraging theory into the information and 
knowledge extraction arena required quantitative treatment of 
the information gained from the efforts made for searching it. 
Such treatment is crucial for assessing the relative performance 
obtained from the implementation of the different strategies. 
Pirolli and Card,(1999) provided the following simplified 
expression for assessing relationships between the time invested 
in producing the information items and the information gain. 
G = gT b /t b (1) 
Where, T b is the total amount of processing time, t b is the 
average processing time per information item and g is the 
average information gain per item. 
In the next section the treatment of information gain from 
developing and implementing expert systems in remote sensing 
is presented. 
5. ESTIMATING RECOGNITION ENERGY AND GAIN 
FROM INFERENCE 
The complexity of the recognition task is determined by the 
level of confusion between the different surface phenomena as 
their appear in the multi-temporal and /or multi-spectral and or 
multi-resolution feature space. In other words: highly separable 
classes would not require much work in recognizing them and 
vice versa. There are numerous unsupervised classification 
algorithms which may facilitate 'automatic' determination of 
classes in the feature space. The inherent separability which 
exists inbetween these classes is inversely related to the "effort" 
needed to be invested in constructing the knowledge base.