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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemtc-m21b.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34P/3KFRQ6S
Repositorysid.inpe.br/mtc-m21b/2015/10.27.16.52   (restricted access)
Last Update2015:10.27.16.53.28 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m21b/2015/10.27.16.52.24
Metadata Last Update2018:06.04.02.55.47 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1117/12.2194135
Citation KeyArabiFernPiza:2015:HMHySp
TitleHMM for hyperspectral spectrum representation and classification with endmember entropy vectors
Year2015
Access Date2024, May 11
Secondary TypePRE CI
Number of Files1
Size232 KiB
2. Context
Author1 Arabi, Samir Youssif Wehbi
2 Fernandes, David
3 Pizarro, Marco Antônio
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHQH
Group1
2
3 DEA-ETE-INPE-MCTI-GOV-BR
Affiliation1 Instituto Federal de Educação, Ciência e Tecnologia de Goías (IFG)
2 Instituto Tecnológico de Aeronáutica (ITA)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 samir.arabi@ifg.edu.br
2
3 marco.pizarro@inpe.br
Conference NameImage and Signal Processing for Remote Sensing, 21
Conference LocationToulouse, France
Date21 Sept.
Book TitleProceedings
History (UTC)2015-10-27 16:52:24 :: simone -> administrator ::
2018-06-04 02:55:47 :: administrator -> simone :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsHyperspectral
image classification
HMM
entropy
AbstractThe Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.
AreaETES
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDEA > HMM for hyperspectral...
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4. Conditions of access and use
Target File96430P.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGPCW/3F6GF6B
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.55.18 2
sid.inpe.br/bibdigital/2013/11.04.22.00 1
Host Collectionsid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination e-mailaddress edition editor format isbn issn label language lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume
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