Mapping urban green from IKONOS data by an object-oriented knowledge-base and fuzzy logic

Demetre, A; Panos, D

dc.contributor.author	Demetre, A	en
dc.contributor.author	Panos, D	en
dc.date.accessioned	2014-03-01T02:49:14Z
dc.date.available	2014-03-01T02:49:14Z
dc.date.issued	2002	en
dc.identifier.issn	0277786X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34437
dc.subject	eCognition	en
dc.subject	Fuzzy logic	en
dc.subject	Land use	en
dc.subject	Object-oriented knowledge base	en
dc.subject	Remote sensing	en
dc.subject	Urban green	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Image analysis	en
dc.subject.other	Image segmentation	en
dc.subject.other	Knowledge based systems	en
dc.subject.other	Land use	en
dc.subject.other	Mapping	en
dc.subject.other	Object oriented programming	en
dc.subject.other	Urban planning	en
dc.subject.other	Vegetation	en
dc.subject.other	Object oriented image analysis	en
dc.subject.other	Urban green	en
dc.subject.other	Remote sensing	en
dc.title	Mapping urban green from IKONOS data by an object-oriented knowledge-base and fuzzy logic	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1117/12.463281	en
heal.identifier.secondary	http://dx.doi.org/10.1117/12.463281	en
heal.publicationDate	2002	en
heal.abstract	Urban green is recognized as an important functional element of the city, which affects directly the standard of living. The present paper is concerned with the study of urban green by means of object-oriented image analysis of high-resolution IKONOS data. More specifically, the potential for detecting urban green and quantitatively assessing it was explored. The analysis included two levels of segmentation and classification. On the first level, objects to which the image was segmented were subsequently classified according to a vegetation index (Scaled MSAVI) to areas with dense, thin or no vegetation. On the second level the image was classified in larger areas that simulated building blocks according to the relative area of vegetation, in order to create a thematic map of urban green density. The evaluation of the results indicated that detection and quantitative assessment of urban green was achieved with satisfactory accuracy. The use of additional data (DEM, hyperspectral, GIS) will allow a more detail study of the urban green from high resolution data by means of object-oriented image analysis.	en
heal.journalName	Proceedings of SPIE - The International Society for Optical Engineering	en
dc.identifier.doi	10.1117/12.463281	en
dc.identifier.volume	4886	en
dc.identifier.spage	96	en
dc.identifier.epage	106	en