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dc.contributor.authorAlam, Fahim Irfan
dc.contributor.authorZhou, Jun
dc.contributor.authorLiew, Alan Wee-Chung
dc.contributor.authorJia, Xiuping
dc.contributor.editorChanglin Wang, Qihao Weng
dc.date.accessioned2017-06-08T05:48:48Z
dc.date.available2017-06-08T05:48:48Z
dc.date.issued2016
dc.identifier.isbn9781509033324
dc.identifier.issn2153-6996
dc.identifier.doi10.1109/IGARSS.2016.7730798
dc.identifier.urihttp://hdl.handle.net/10072/339311
dc.description.abstractThis paper proposes a method that uses both spectral and spatial information to segment remote sensing hyperspectral images. After a hyperspectral image is over-segmented into superpixels, a deep Convolutional Neural Network (CNN) is used to perform superpixel-level labelling. To further delineate objects from a hyperspectral scene, this paper attempts to combine the properties of CNN and Conditional Random Field (CRF). A mean-field approximation algorithm for CRF inference is used and formulated with Gaussian pairwise potentials as Recurrent Neural Network. This combined network is then plugged into the CNN which leads to a deep network that has robust characteristics of both CNN and CRF. Preliminary results suggest the usefulness of this framework to a promising extent.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename36th IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.relation.ispartofconferencetitle2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
dc.relation.ispartofdatefrom2016-07-10
dc.relation.ispartofdateto2016-07-15
dc.relation.ispartoflocationBeijing, PEOPLES R CHINA
dc.relation.ispartofpagefrom6890
dc.relation.ispartofpagefrom4 pages
dc.relation.ispartofpageto6893
dc.relation.ispartofpageto4 pages
dc.relation.ispartofvolume2016-November
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchcode460299
dc.titleCRF learning with CNN features for hyperspectral image segmentation
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, School of Information and Communication Technology
gro.rights.copyright© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorLiew, Alan Wee-Chung
gro.griffith.authorZhou, Jun


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