The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm - Two successful approaches to improving SPOT-XS classification

Kontoes, CC; Rokos, D

dc.contributor.author	Kontoes, CC	en
dc.contributor.author	Rokos, D	en
dc.date.accessioned	2014-03-01T01:12:23Z
dc.date.available	2014-03-01T01:12:23Z
dc.date.issued	1996	en
dc.identifier.issn	0143-1161	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12090
dc.subject	Knowledge Based System	en
dc.subject	Spatial Context	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Algorithms	en
dc.subject.other	Feature extraction	en
dc.subject.other	Image processing	en
dc.subject.other	Image quality	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Land use	en
dc.subject.other	Logic programming	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Probability	en
dc.subject.other	Dempster Shafer reasoning scheme	en
dc.subject.other	Spatial context information	en
dc.subject.other	Supervised relaxation algorithm	en
dc.subject.other	Remote sensing	en
dc.subject.other	classification	en
dc.subject.other	image classification	en
dc.subject.other	knowledge based system	en
dc.subject.other	land use class	en
dc.subject.other	SPOT-XS	en
dc.subject.other	supervised relaxation algorithm	en
dc.subject.other	texture	en
dc.title	The integration of spatial context information in an experimental knowledge-based system and the supervised relaxation algorithm - Two successful approaches to improving SPOT-XS classification	en
heal.type	journalArticle	en
heal.identifier.primary	10.1080/01431169608949132	en
heal.identifier.secondary	http://dx.doi.org/10.1080/01431169608949132	en
heal.language	English	en
heal.publicationDate	1996	en
heal.abstract	This paper describes two different methods which integrate contextual information in a classification process. This process aims to refine the map products given by the application of a common parametric classification algorithm. The first method is the well known Supervised Relaxation Algorithm, and makes use of the first classification, with additional contextual information. The contextual information is derived either from texture features or from other map products introducing additional information on the existing land use classes. The second method is a knowledge-based system, which makes use of image and geographical context rules. The probability figures, derived from the image classifier and the rule base are combined by the use of the Dempster-Shafer reasoning scheme. Experiments using satellite data from the Loir et Cher region (Central France), together with the appropriate ground truth data, have shown that both methods return improved classification products in terms of thematic and statistical accuracy, compared to using a parametric image classifier alone.	en
heal.publisher	TAYLOR & FRANCIS LTD	en
heal.journalName	International Journal of Remote Sensing	en
dc.identifier.doi	10.1080/01431169608949132	en
dc.identifier.isi	ISI:A1996VL98100002	en
dc.identifier.volume	17	en
dc.identifier.issue	16	en
dc.identifier.spage	3093	en
dc.identifier.epage	3106	en