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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/3SFT7JL
Repositorysid.inpe.br/mtc-m21c/2019/01.02.13.06   (restricted access)
Last Update2019:01.02.13.06.23 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2019/01.02.13.06.23
Metadata Last Update2019:01.14.17.06.42 (UTC) administrator
DOI10.1080/17538947.2018.1474958
ISSN1753-8947
Citation KeyNegriFreSilMenDut:2018:ReClPo
TitleRegion-based classification of PolSAR data using radial basis kernel functions with stochastic distances
Year2018
Access Date2024, Apr. 28
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size5222 KiB
2. Context
Author1 Negri, Rogério Galante
2 Frery, Alejandro C.
3 Silva, Wagner B.
4 Mendes, Tatiana S. G.
5 Dutra, Luciano Vieira
Resume Identifier1
2
3
4
5 8JMKD3MGP5W/3C9JHMA
ORCID1 0000-0002-4808-2362
2 0000-0002-8002-5341
3 0000-0002-5686-5105
4 0000-0002-0421-5311
5 0000-0002-7757-039X
Group1
2
3
4
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Affiliation1 Universidade Estadual Paulista (UNESP)
2 Universidade Federal de Alagoas (UFAL)
3 Instituto Militar de Engenharia (IME)
4 Universidade Estadual Paulista (UNESP)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4
5 luciano.dutra@inpe.br
JournalInternational Journal of Digital Earth
Volume2018
History (UTC)2019-01-02 13:07:31 :: simone -> administrator :: 2018
2019-01-14 17:06:42 :: administrator -> simone :: 2018
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsPolSAR
image classification
stochastic distance
minimum distance classifier
SVM
AbstractRegion-based classification of PolSAR data can be effectively performed by seeking for the assignment that minimizes a distance between prototypes and segments. Silva et al. [Classification of segments in PolSAR imagery by minimum stochastic distances between wishart distributions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6 (3): 12631273] used stochastic distances between complex multivariate Wishart models which, differently from other measures, are computationally tractable. In this work we assess the robustness of such approach with respect to errors in the training stage, and propose an extension that alleviates such problems. We introduce robustness in the process by incorporating a combination of radial basis kernel functions and stochastic distances with Support Vector Machines (SVM). We consider several stochastic distances between Wishart: Bhatacharyya, Kullback-Leibler, Chi-Square, Rényi, and Hellinger. We perform two case studies with PolSAR images, both simulated and from actual sensors, and different classification scenarios to compare the performance of Minimum Distance and SVM classification frameworks. With this, we model the situation of imperfect training samples. We show that SVM with the proposed kernel functions achieves better performance with respect to Minimum Distance, at the expense of more computational resources and the need of parameter tuning. Code and data are provided for reproducibility.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Region-based classification of...
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agreement Directory Content
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4. Conditions of access and use
Languageen
Target Filenegri_region.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.53.50 1
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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