A Comparison Of Neural Network And Expert System Methods For Analysis Of Remotely-sensed Imagery

Wilkinson, G; Kanellopoulos, I; Kontoes, C; Megier, J

dc.contributor.author	Wilkinson, G	en
dc.contributor.author	Kanellopoulos, I	en
dc.contributor.author	Kontoes, C	en
dc.contributor.author	Megier, J	en
dc.date.accessioned	2014-03-01T02:48:05Z
dc.date.available	2014-03-01T02:48:05Z
dc.date.issued	1992	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/33519
dc.subject	Method of Image	en
dc.subject	Performance Improvement	en
dc.subject	Remote Sensing Imagery	en
dc.subject	Expert System	en
dc.subject	Neural Network	en
dc.subject	Rule Based	en
dc.title	A Comparison Of Neural Network And Expert System Methods For Analysis Of Remotely-sensed Imagery	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IGARSS.1992.576627	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IGARSS.1992.576627	en
heal.publicationDate	1992	en
heal.abstract	This paper describes an experimental comparison which has been made between two alternative methods of image classification: one based on a neural network and the other on a rule-based expert system. Both methods were applied to the same image data. The results show that both methods give useful performance improvements in comparison with more traditional parametric classifiers. It was also	en
heal.journalName	Geoscience and Remote Sensing IEEE International Symposium	en
dc.identifier.doi	10.1109/IGARSS.1992.576627	en