1085 
'7. Beijing 2008 
KNOWLEDGE BASED EXPERT SYSTEMS IN REMOTE SENSING TASKS: 
QUANTIFYING GAIN FROM INTELLIGENT INFERENCE 
M. Shoshany 
Geoinformation Engineering, Faculty of Civil & Environmental Engineering, Technion, Israel Institute of Technology, 
Haifa 32000, Israel - maximsh@tx.technion.ac.il 
Commission VII, WG VII/6 
KEY WORDS: Knowledge Base, Reasoning, Theory, Fusion, Classification, Data Mining 
ABSTRACT: 
How to measure gain from the use of intelligent inference ? How can the complexity of the recognition/ classification task be 
estimated? What is the type of evidence which best suits an .inference mechanism? These questions are addressed here in their 
theoretical and methodological aspects. Their practical implications are demonstrated with 'real' crop mapping task. For this 
purpose, simple rule-based system is compared with expert system based on Dempster - Shafer evidential reasoning algorithm. 
The advantage of using 'soft' / implicit evidence with the Dempster-Shafer algorithm over the use of 'hard' / explicit evidence with 
decision - tree type procedure is discussed. 
1. INTRODUCTION 
Spatial, temporal and spectral complexity of remote sensing 
recognition tasks necessitates the use of Knowledge Based 
Expert Systems (KBES). Such complexity concern the fact that 
the same surface phenomenon may emerge in different ways in 
imagery sources (see discussion for example in Lu and 
Weng,(2007).). These systems facilitate algorithmic 
adjustments of the use of classification or recognition rules 
according to the local context. KBES combine available 
evidence, procedural knowledge regarding priorities in 
implementing evidences in parallel or sequentially; and an 
inference mechanism. The procedural knowledge may be 
constructed based on human expert knowledge or through 
extensive learning of the recognition problem at hand. Data 
Mining and Knowledge Discovery are technologies suitable for 
conducting such extensive learning. In parallel, there are 
numerous inference mechanisms available which shift the 
emphasis into designing efficient expert systems considering 
different strategies for information extraction and processing. 
Such strategies also concern the efforts made in the search for 
evidence on the one hand and the information gain from using 
this evidence on the other hand. Recently, there is growing 
interest in the field of information seeking and utilization with 
the adoption of Evolutionary - Ecological Models of Foraging 
(Pirolli and Card, 1999). Comparison between strategies based 
on assessment of information gain versus information cost is a 
central element in the Information Foraging Theory. A primary 
problem in adopting this theory in remote sensing concern 
quantitative evaluation of gain from evidence versus gain from 
inference (Shoshany and Cohen,2007). The presentation in the 
conference would be divided into three elements: review of 
fundamental terms, concepts and strategies in the 
implementation of KBES, assessment of the similarity between 
information foraging and remote sensing tasks; and finally, 
discussion of the ways to estimate gain from evidence and 
inference in mapping missions. 
2. FUNDAMENTAL TERMINOLOGY 
First, it is important to recognize that at the initial stage of the 
recognition task we are dealing with propositions, or better say 
multiple propositions for each recognition task. Evidence is 
then defined (Wikipedia) as “any objectively observable or 
demonstrable circumstance which tends to indicate or disprove 
a proposition". Two main types of evidence can be discerned: 
Explicit Evidence: refers to the "notion of plain distinct 
expression that leaves no need to infer" (Merriam-Webster). 
In the remote-sensing narrow band signatures of specific 
materials and very unique combinations of spectral 
reflectance thresholds are examples of explicit evidence. 
The production of such evidence would typically require 
exhaustive search (see methods proposed by Peddle and 
Ferguson,2002). 
Implicit Evidence: type of proposition represents a 
conclusion "capable of being understood from something 
else though unexpressed: capable of being inferred" 
(Merriam-Webster). 
Implicit evidence would consist of generalizations, 
associations and contextual information representing some 
level of non-uniqueness and conflicts with reference to the 
object subject to recognition (e.g., Cohen and 
Shoshany,2005). While the production of such evidence 
might be relatively easy, its use would require inference 
capable of overcoming such non-uniqueness and conflicts. 
Considering that remote sensing evidence would be usually 
located on a continuous scale inbetween the extreme explicit to 
extreme implicitness, it is clear that the role of inference is 
elementary. Inference is defined (Merriam-Webster) as "the 
act of passing from one or more propositions, statements, or 
judgments considered as true to another the truth of which is 
believed to follow from that of the former". Deductive, 
inductive, abductive, analogical or common-sense reasoning 
would facilitate such inference (Durkin, 1994). The selection of 
type of evidence and inference is a crucial step in developing 
KBES strategies.