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dc.contributor.authorAl-Khafaji, Suhad Lateef
dc.contributor.authorZia, Ali
dc.contributor.authorZhou, Jun
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.editorGuo, Y
dc.contributor.editorLi, H
dc.contributor.editorCai, W
dc.contributor.editorMurshed, M
dc.contributor.editorWang, Z
dc.contributor.editorGao, J
dc.contributor.editorFeng, DD
dc.date.accessioned2018-07-03T01:32:12Z
dc.date.available2018-07-03T01:32:12Z
dc.date.issued2017
dc.identifier.isbn9781538628393
dc.identifier.doi10.1109/DICTA.2017.8227462
dc.identifier.urihttp://hdl.handle.net/10072/378008
dc.description.abstractBoundary detection in hyperspectral image (HSI) is a challenging task due to high data dimensionality and the that is distributed over the spectral bands. For this reason, there is a dearth of research on boundary detection in HSI. In this paper, we propose a spectral-spatial feature based statistical co-occurrence method for this task. We adopt probability density function (PDF) to estimate the co-occurrence of features at neighboring pixel pairs. Such cooccurrence is rare at the boundary and repeated within a region. To fully explore the material information embedded in HSI, joint spectral-spatial features are extracted at each pixel. The PDF values are then used to construct an affinity matrix for all pixels. After that, a spectral clustering algorithm is applied on the affinity matrix to produce boundaries. Our algorithm is evaluated on a dataset of real-world HSIs and compared with two alternative approaches. The results show that the proposed method is very effective in exploring object boundaries from HSI images.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameInternational Conference on Digital Image Computing - Techniques and Applications (DICTA)
dc.relation.ispartofconferencetitle2017 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING - TECHNIQUES AND APPLICATIONS (DICTA)
dc.relation.ispartofdatefrom2017-11-29
dc.relation.ispartofdateto2017-12-01
dc.relation.ispartoflocationSydney, AUSTRALIA
dc.relation.ispartofpagefrom552
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto558
dc.relation.ispartofpageto7 pages
dc.relation.ispartofvolume2017-December
dc.subject.fieldofresearchArtificial Intelligence and Image Processing not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode080199
dc.subject.fieldofresearchcode0801
dc.titleMaterial Based Boundary Detection in Hyperspectral Images
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.hasfulltextNo Full Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorZhou, Jun
gro.griffith.authorZia, Ali
gro.griffith.authorAl-Khafaji, Suhad


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