Monitoring urban changes based on scale-space filtering and object-oriented classification

Doxani, G; Karantzalos, K; Tsakiri-Strati, M

dc.contributor.author	Doxani, G	en
dc.contributor.author	Karantzalos, K	en
dc.contributor.author	Tsakiri-Strati, M	en
dc.date.accessioned	2014-03-01T02:11:28Z
dc.date.available	2014-03-01T02:11:28Z
dc.date.issued	2012	en
dc.identifier.issn	15698432	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29908
dc.subject	Morphological scale space filtering	en
dc.subject	Multivariate alteration detection	en
dc.subject	Object-based image analysis	en
dc.subject.other	algorithm	en
dc.subject.other	detection method	en
dc.subject.other	environmental monitoring	en
dc.subject.other	image analysis	en
dc.subject.other	image classification	en
dc.subject.other	image resolution	en
dc.subject.other	multivariate analysis	en
dc.subject.other	sensor	en
dc.subject.other	urban area	en
dc.title	Monitoring urban changes based on scale-space filtering and object-oriented classification	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.jag.2011.07.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.jag.2011.07.002	en
heal.publicationDate	2012	en
heal.abstract	This paper introduces a multi-temporal image processing framework towards an efficient and (semi-) automated detection of urban changes. Nonlinear scale space filtering was embedded in an object-based classification procedure and the resulted simplified images provided a more compact and reliable source in order to generate image objects in various scales. In this manner the multiresolution segmentation outcome was constrained qualitatively. Multivariate alteration detection (MAD) transformation was applied afterwards on the simplified data to facilitate the detection of possible changes. The altered image regions along with the simplified data were further analyzed through a multilevel knowledge-based classification scheme. The developed algorithm was implemented on a number of multi-temporal data acquired by different remote sensing sensors. The qualitative and quantitative evaluation of change detection results performed with the help of the appropriate ancillary ground truth data. Experimental results demonstrated the effectiveness of the developed scale-space, object-oriented classification framework. © 2011 Elsevier B.V.	en
heal.journalName	International Journal of Applied Earth Observation and Geoinformation	en
dc.identifier.doi	10.1016/j.jag.2011.07.002	en
dc.identifier.volume	15	en
dc.identifier.issue	1	en
dc.identifier.spage	38	en
dc.identifier.epage	48	en