A knowledge-based system for extraction and recognition of linear features in high resolution remotely-sensed imagery

Peacegood, Gillian (1989) A knowledge-based system for extraction and recognition of linear features in high resolution remotely-sensed imagery. (PhD thesis), Kingston Polytechnic.

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Abstract

A knowledge-based system for the automatic extraction and recognition of linear features from digital imagery has been developed, with a knowledge base applied to the recognition of linear features in high resolution remotely sensed imagery, such as SPOT HRV and XS, Thematic Mapper and high altitude aerial photography. In contrast to many knowledge-based vision systems, emphasis is placed on uncertainty and the exploitation of context via statistical inferencing techniques, and issues of strategy and control are given less emphasis. Linear features are extracted from imagery, which may be multiband imagery, using an edge detection and tracking algorithm. A relational database for the representation of linear features has been developed, and this is shown to be useful in a number of applications, including general purpose query and display. A number of proximity relationships between the linear features in the database are established, using computationally efficient algorithms. Three techniques for classifying the linear features by exploiting uncertainty and context have been implemented and are compared. These are Bayesian inferencing using belief networks, a new inferencing technique based on belief functions and relaxation labelling using belief functions. The two inferencing techniques are shown to produce more realistic results than probabilistic relaxation, and the new inferericing method based on belief functions to perform best in practical situations. Overall, the system is shown to produce reasonably good classification results on hand extracted linear features, although the classification is less good on automatically extracted linear features because of shortcomings in the edge detection and extraction processes. The system adopts many of the features of expert systems, including complete separation of control from stored knowledge and justification for the conclusions reached.

Item Type: Thesis (PhD)
Physical Location: This item is held in stock at Kingston University Library.
Depositing User: Automatic Import Agent
Date Deposited: 09 Sep 2011 21:38
Last Modified: 29 May 2014 09:28
URI: http://eprints.kingston.ac.uk/id/eprint/20529

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