Object-based image analysis through nonlinear scale-space filtering

Tzotsos, A; Karantzalos, K; Argialas, D

dc.contributor.author	Tzotsos, A	en
dc.contributor.author	Karantzalos, K	en
dc.contributor.author	Argialas, D	en
dc.date.accessioned	2014-03-01T01:36:32Z
dc.date.available	2014-03-01T01:36:32Z
dc.date.issued	2011	en
dc.identifier.issn	0924-2716	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/21325
dc.subject	Analysis	en
dc.subject	Automation	en
dc.subject	Classification	en
dc.subject	Segmentation	en
dc.subject	Simplification	en
dc.subject.classification	Geography, Physical	en
dc.subject.classification	Geosciences, Multidisciplinary	en
dc.subject.classification	Remote Sensing	en
dc.subject.classification	Imaging Science & Photographic Technology	en
dc.subject.other	Analysis	en
dc.subject.other	Classification	en
dc.subject.other	Classification framework	en
dc.subject.other	Classification procedure	en
dc.subject.other	Data sets	en
dc.subject.other	Image objects	en
dc.subject.other	Kernel-based learning	en
dc.subject.other	Levelings	en
dc.subject.other	Multi-spectral	en
dc.subject.other	Multiscale segmentation	en
dc.subject.other	Nonlinear scale	en
dc.subject.other	Nonlinear scale space	en
dc.subject.other	Object based image analysis	en
dc.subject.other	Object extraction	en
dc.subject.other	Object oriented	en
dc.subject.other	Observation data	en
dc.subject.other	Passive remote sensing	en
dc.subject.other	Quantitative evaluation	en
dc.subject.other	Remote sensing data	en
dc.subject.other	SAR Images	en
dc.subject.other	Scale-space representation	en
dc.subject.other	Segmentation	en
dc.subject.other	Simplification	en
dc.subject.other	Space-borne sensor	en
dc.subject.other	Spectral properties	en
dc.subject.other	Very high resolution	en
dc.subject.other	Adaptive filtering	en
dc.subject.other	Classification (of information)	en
dc.subject.other	Crystal orientation	en
dc.subject.other	Image analysis	en
dc.subject.other	Image classification	en
dc.subject.other	Image segmentation	en
dc.subject.other	Nonlinear analysis	en
dc.subject.other	Remote sensing	en
dc.subject.other	Space optics	en
dc.subject.other	Quality control	en
dc.subject.other	aerial photograph	en
dc.subject.other	anisotropy	en
dc.subject.other	automation	en
dc.subject.other	data set	en
dc.subject.other	image analysis	en
dc.subject.other	image classification	en
dc.subject.other	nonlinearity	en
dc.subject.other	remote sensing	en
dc.subject.other	satellite data	en
dc.subject.other	segmentation	en
dc.title	Object-based image analysis through nonlinear scale-space filtering	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.isprsjprs.2010.07.001	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.isprsjprs.2010.07.001	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach. (C) 2010 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	ISPRS Journal of Photogrammetry and Remote Sensing	en
dc.identifier.doi	10.1016/j.isprsjprs.2010.07.001	en
dc.identifier.isi	ISI:000286854900001	en
dc.identifier.volume	66	en
dc.identifier.issue	1	en
dc.identifier.spage	2	en
dc.identifier.epage	16	en