On automatic absorption detection for Imaging spectroscopy: A comparative study

dc.contributor.authorFu, Zhouyu
dc.contributor.authorRobles-Kelly, Antonio
dc.contributor.authorCaelli, Terry
dc.contributor.authorTan, Robby
dc.date.accessioned2015-12-10T22:27:01Z
dc.date.issued2007
dc.date.updated2015-12-09T09:36:57Z
dc.description.abstractIn this paper, we aim at presenting a survey on automatic absorption recovery methods for imaging spectroscopy. We commence by viewing the algorithms in the literature from a technical perspective and presenting an overview of the derivative analysis, fingerprint, and maximum modulus wavelet transform techniques. In addition to these methods, we also present a novel absorption recovery approach based upon unimodal regression and continuum removal. With this technical review of the methods under study, we perform a complexity analysis and examine the implementation issues pertaining to each of the alternatives. We show how detected absorption bands can be used for purposes of material identification. We conclude this paper by providing a performance study and providing identification results on hyperspectral imagery. To this end, we make use of a number of distance measures to evaluate the quality of the recovered absorptions, as compared to continuum-removed spectra.
dc.identifier.issn0196-2892
dc.identifier.urihttp://hdl.handle.net/1885/54014
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Geoscience and Remote Sensing
dc.subjectKeywords: Absorption recovery; Hyperspectral image processing; Hyperspectral imagery; Maximum modulus wavelet transforms (MMWT); Unimodal segmentation; Absorption spectra; Image quality; Image sensors; Imaging systems; Regression analysis; Wavelet transforms; Absor Absorption features; Fingerprint; Hyperspectral image processing; Maximum modulus wavelet transform (MMWT); Unimodal segmentation
dc.titleOn automatic absorption detection for Imaging spectroscopy: A comparative study
dc.typeJournal article
local.bibliographicCitation.issue11
local.bibliographicCitation.lastpage3844
local.bibliographicCitation.startpage3827
local.contributor.affiliationFu, Zhouyu , College of Engineering and Computer Science, ANU
local.contributor.affiliationRobles-Kelly, Antonio, College of Engineering and Computer Science, ANU
local.contributor.affiliationCaelli, Terry, College of Engineering and Computer Science, ANU
local.contributor.affiliationTan, Robby, College of Engineering and Computer Science, ANU
local.contributor.authoruidFu, Zhouyu , u4176893
local.contributor.authoruidRobles-Kelly, Antonio, u1811090
local.contributor.authoruidCaelli, Terry, u971266
local.contributor.authoruidTan, Robby, a198505
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationU1408929xPUB289
local.identifier.citationvolume45
local.identifier.doi10.1109/TGRS.2007.903402
local.identifier.scopusID2-s2.0-35648962961
local.type.statusPublished Version

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