Object-based image analysis through nonlinear scale-space filtering

DSpace/Manakin Repository

Show simple item record

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 http://hdl.handle.net/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

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record