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Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Cavallaro, Gabriele
dc.contributor.author Dalla Mura, Mauro
dc.contributor.author Plaza, Antonio
dc.date.accessioned 2016-08-11T10:52:52Z
dc.date.available 2016-08-11T10:52:52Z
dc.date.issued 2016
dc.date.submitted 2015
dc.identifier.citation Gabriele Cavallaro, Mauro Dalla Mura, Jón Atli Benediktsson, Antonio Plaza. "Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes." IEEE Transactions on Geoscience and Remote Sensing (Volume:54, Issue:7)
dc.identifier.issn 0196-2892
dc.identifier.uri https://hdl.handle.net/20.500.11815/59
dc.description Post-print. Lokaútgáfa höfunda
dc.description.abstract Remotely sensed images with very high spatial resolution provide a detailed representation of the surveyed scene with a geometrical resolution that, at the present, can be up to 30 cm (WorldView-3). A set of powerful image processing operators have been defined in the mathematical morphology framework. Among those, connected operators [e.g., attribute filters (AFs)] have proven their effectiveness in processing very high resolution images. AFs are based on attributes which can be efficiently implemented on tree-based image representations. In this paper, we considered the definition of min, max, direct, and subtractive filter rules for the computation of AFs over the tree-of-shapes representation. We study their performance on the classification of remotely sensed images. We compare the classification results over the tree of shapes with the results obtained when the same rules are applied on the component trees. The random forest is used as a baseline classifier, and the experiments are conducted using multispectral data sets acquired by QuickBird and IKONOS sensors over urban areas.
dc.description.sponsorship EU FP7 Theme Space project North State, The program J. Verne 2014 under Project 31936TD, and the INS2I JCJC 2015 under project “IDES”
dc.format.extent 3899 - 3911
dc.language.iso en
dc.publisher IEEE Geoscience & Remote Sensing Society
dc.relation info:eu-repo/grantAgreement/EC/FP7/606962
dc.rights info:eu-repo/semantics/openAccess
dc.subject Gray-scale
dc.subject Morphology
dc.subject Remote sensing
dc.subject Shape
dc.subject Spatial resolution
dc.subject Vegetation
dc.title Remote Sensing Image Classification Using Attribute Filters Defined over the Tree of Shapes
dc.type info:eu-repo/semantics/article
dcterms.license (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.description.version Peer Reviewed
dc.identifier.journal IEEE Transactions on Geoscience and Remote Sensing
dc.relation.url DOI: 10.1109/TGRS.2016.2530690
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)


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