Open Access Repository

The nature and acquisition of expert knowledge to be used in spatial expert systems for classifying remotely sensed images

Downloads

Downloads per month over past year

Crowther, Paul (1999) The nature and acquisition of expert knowledge to be used in spatial expert systems for classifying remotely sensed images. PhD thesis, University of Tasmania.

[img]
Preview
PDF (Whole thesis)
whole_CrowtherP...pdf | Download (29MB)
Available under University of Tasmania Standard License.

| Preview

Abstract

Knowledge engineering is the process of acquiring expert knowledge from human
domain experts. In this thesis the emphasis is on the acquisition of geographic or
spatial knowledge from experts involved in interpreting multi-spectral satellite
images.
This thesis argues that spatial knowledge is primarily visual, hence tools to acquire it
also need to be visual. Currently there is no methodology, other than ad hoc interview
and protocol analysis, for acquiring expert knowledge of interpretation of satellite
images. As a result, there cannot be an integrated knowledge acquisition toolkit, since
this must be based on a formal methodology. This thesis offers a methodology to
overcome this shortcoming and presents a series of tools to implement the
methodology.
In the first part of the thesis the nature of geographic knowledge is investigated. A
geographic knowledge classification scheme is presented as the basis of the work in
the rest of the thesis. It is shown that geographic knowledge can be divided into a six
level hierarchy:
• Primitive knowledge about point, line and areal objects,
• Relationship knowledge about the relationships between primitive objects,
• Assembly knowledge about related collections of primitive objects,
• Non-Visual knowledge of expert heuristics (knowledge of short cuts acquired by
experience),
• Consolidation knowledge of how to resolve and evaluate conflicting information
and
• Interpretation knowledge of how to combine the other knowledge types to produce
a classified image.
This six level hierarchical classification of geographic knowledge forms the basis of
the KAGES (Knowledge Acquisition for Geographic Expert Systems) methodology.
Traditional knowledge acquisition procedures are studied and their relevance to a
geographic domain discussed. This includes both human interaction techniques such as interviewing and automated knowledge acquisition methods such as neural
networks and machine learning. It will be shown that although automated pattern
recognition techniques are important, there is still a need to include knowledge
acquired by human image interpreters in an automated image interpretation system.
There is a theoretical discussion of new techniques to acquire visual knowledge of the
types identified in the KAGES methodology. It is shown that these methods can be
combined into an integrated knowledge engineering toolkit to acquire geographic
knowledge from satellite image interpreters. Not all geographic knowledge is visual
however. Three types of non-visual knowledge, algorithmic, heuristic and temporal,
are identified and investigated. The first two are implemented in the knowledge
engineering toolkit described in this thesis.
It is shown that if there are multiple domain experts and multiple knowledge
acquisition sessions multiple knowledge-bases will be produced. Techniques for the
consolidation of these knowledge-bases is presented.
The final section of the thesis involves evaluation of KAGES. This is done in two
ways: user evaluation and application of the methodology in two domains. The user
evaluation of the KAGES methodology and toolkit involved a number of image
interpretation experts from a variety of domains and currently using a variety of tools.
They were questioned about the usefulness and useability of the KAGES toolkit. The results of using the tools in the toolkit are evaluated by generating rules for two
scenarios, one for sea ice identification and the other for crop recognition. The rules
produced using the toolkit are compared with rules produced using other techniques.
The effect of applying rules generated by the toolkit to classify images is compared
with the results from other image classification methods.

Item Type: Thesis (PhD)
Keywords: Spatial systems, Geographic information systems, Knowledge acquisition (Expert systems)
Copyright Holders: The Author
Copyright Information:

Copyright 1999 the Author - The University is continuing to endeavour to trace the copyright
owner(s) and in the meantime this item has been reproduced here in good faith. We
would be pleased to hear from the copyright owner(s).

Additional Information:

Thesis (PhD.) --University of Tasmania, 1999. Includes bibliographical references

Date Deposited: 09 Dec 2014 00:04
Last Modified: 09 May 2016 06:52
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP