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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/49QDKK8
Repositorysid.inpe.br/mtc-m21d/2023/09.11.13.40
Metadata Repositorysid.inpe.br/mtc-m21d/2023/09.11.13.40.21
Metadata Last Update2024:01.02.17.16.46 (UTC) administrator
DOI10.1080/2150704X.2023.2255348
ISSN2150-704X
Citation KeyOliveiraDutrSant:2023:NoQuAs
TitleA novel quality assessment index for evaluating the spatial uniformity of remote sensing image classification
Year2023
Access Date2024, May 20
Type of Workjournal article
Secondary TypePRE PI
2. Context
Author1 Oliveira, Willian Vieira de
2 Dutra, Luciano Vieira
3 Sant'Anna, Sidnei João Siqueira
Resume Identifier1
2 8JMKD3MGP5W/3C9JHMA
3 8JMKD3MGP5W/3C9JJ8N
ORCID1 0000-0002-7612-9121
2 0000-0002-7757-039X
3 0000-0001-7557-3215
Group1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
3 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 willian.oliveira@inpe.br
2 luciano.dutra@inpe.br
3 sidnei.santanna@inpe.br
JournalRemote Sensing Letters
Volume14
Number9
Pages958-967
Secondary MarkA2_GEOGRAFIA B2_INTERDISCIPLINAR B3_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS
History (UTC)2023-09-11 13:40:21 :: simone -> administrator ::
2023-09-11 13:40:21 :: administrator -> simone :: 2023
2023-09-11 13:40:33 :: simone -> administrator :: 2023
2024-01-02 17:16:46 :: administrator -> simone :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
AbstractAccuracy assessment techniques have always been considered essential to evaluate the quality and reliability of image classifications. They are commonly based on metrics extracted from the confusion matrix. Application fields whose source data are organized in a two-dimensional disposition, such as remote sensing imagery, generally present their classification outcomes in the same format. However, the spatial organization of the class objects is typically neglected for accuracy evaluation. We propose an additional quality index for measuring the uniformity ratio (UR) of remote sensing image classifications. When visually examining image classification outcomes, we tend to give lower reliability to results that present higher non-uniformity of class objects, frequently known as classification noise. The basic hypothesis of this paper is that classification models that better delineates the class signature regions, in the available feature space, produce less spatial variation of class assignments to the mapping units (normally pixels). The experimental results indicate that the proposed index can be efficiently incorporated into remote sensing applications to complement standard accuracy metrics.
AreaSRE
Arrangement 1urlib.net > Produção pgr ATUAIS > CAP > A novel quality...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > A novel quality...
Arrangement 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > A novel quality...
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4. Conditions of access and use
Languageen
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher denyfinaldraft12
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F2PHGS
8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46KUES5
DisseminationWEBSCI; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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