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dc.contributor.authorAl-khafaji, Suhad Lateef
dc.contributor.authorZhou, Jun
dc.contributor.authorZia, Ali
dc.contributor.authorLiew, Alan Wee-Chung
dc.date.accessioned2019-07-04T12:30:29Z
dc.date.available2019-07-04T12:30:29Z
dc.date.issued2018
dc.identifier.issn1057-7149
dc.identifier.doi10.1109/TIP.2017.2749145
dc.identifier.urihttp://hdl.handle.net/10072/368930
dc.description.abstractSpectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE - Institute of Electrical and Electronics Engineers
dc.publisher.placeUnited States
dc.relation.ispartofpagefrom837
dc.relation.ispartofpageto850
dc.relation.ispartofissue2
dc.relation.ispartofjournalIEEE Transactions on Image Processing
dc.relation.ispartofvolume27
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchComputer vision and multimedia computation
dc.subject.fieldofresearchGraphics, augmented reality and games
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode5204
dc.subject.fieldofresearchcode4603
dc.subject.fieldofresearchcode4607
dc.titleSpectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2018 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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