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1. Identity statement Reference Type Journal Article Site mtc-m21d.sid.inpe.br Holder Code isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S Identifier 8JMKD3MGP3W34T/4874755 Repository sid.inpe.br/mtc-m21d/2022/12.12.18.32 Metadata Repository sid.inpe.br/mtc-m21d/2022/12.12.18.32.15 Metadata Last Update 2023:01.03.16.46.26 (UTC) administrator DOI 10.1080/01431161.2022.2145580 ISSN 0143-1161 Citation Key OliveiraDutrSant:2022:MePrNa Title A meta-methodology for preserving narrow objects when using spatial contextual classifiers for remote sensing data Year 2022 Access Date 2024, May 19 Type of Work journal article Secondary Type PRE PI 2. Context Author 1 Oliveira, Willian Vieira de 2 Dutra, Luciano Vieira 3 Sant'Anna, Sidnei João Siqueira Resume Identifier 1 2 8JMKD3MGP5W/3C9JHMA 3 8JMKD3MGP5W/3C9JJ8N Group 1 CAP-COMP-DIPGR-INPE-MCTI-GOV-BR 2 DIOTG-CGCT-INPE-MCTI-GOV-BR 3 DIOTG-CGCT-INPE-MCTI-GOV-BR Affiliation 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) Author e-Mail Address 1 wivoliveira@yahoo.com.br 2 lvdutra@gmail.com 3 sjssantanna@gmail.com Journal International Journal of Remote Sensing Volume 43 Number 18 Pages 6741-3765 Secondary Mark A1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_INTERDISCIPLINAR A2_GEOGRAFIA A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_ENGENHARIAS_I A2_CIÊNCIAS_AMBIENTAIS A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_GEOCIÊNCIAS B1_ENGENHARIAS_II B1_CIÊNCIAS_AGRÁRIAS_I B1_BIODIVERSIDADE B2_SAÚDE_COLETIVA B2_ODONTOLOGIA B3_CIÊNCIAS_BIOLÓGICAS_I B3_BIOTECNOLOGIA B5_ASTRONOMIA_/_FÍSICA History (UTC) 2022-12-12 18:32:15 :: simone -> administrator :: 2022-12-12 18:32:16 :: administrator -> simone :: 2022 2022-12-12 18:32:54 :: simone -> administrator :: 2022 2022-12-20 10:35:29 :: administrator -> simone :: 2022 2022-12-20 14:18:30 :: simone -> administrator :: 2022 2023-01-03 16:46:26 :: administrator -> simone :: 2022 3. Content and structure Is the master or a copy? is the master Content Stage completed Transferable 1 Content Type External Contribution Version Type publisher Abstract In the field of land-cover classification with remote sensing images, methods that analyse purely the spectral information of individual pixels generally produce noisy results, due to salt-and-pepper effects. The use of methods that also incorporate spatial contextual information into classification, often defined as contextual or spectral-spatial approaches, is an effective strategy for reducing the occurrence of punctual noises and, consequently, improving accuracy. However, contextual methods still present a critical limitation: an over smoothing performance on certain classes can cause the loss of details on important spatial structures. They may overlook salient punctual and linear objects that can be efficiently classified using purely spectral information, particularly over areas of rapid class transition. This issue is commonly observed with the classification of medium spatial-resolution images that include narrow class structures, such as rivers and roads. To solve this problem, we present a strategy for contextual classification that allows adjusting a trade-off between noise smoothing and the preservation of small spatial details. The proposed strategy comprises a meta-methodology, in the sense that it does not depend on specific pixel-based and contextual classifiers. The meta-methodology for improving contextual classification methods (Meta-CTX) consists in performing a separability analysis, at the pixel level, based on the class membership estimates provided by a pixel-based classifier. The Meta-CTX analyses the distance between class membership estimates in order to identify pixels that are expected to be accurately classified using purely spectral information. The Meta-CTX preserves the per-pixel classification of these pixels. It uses spatial contextual information only to classify pixels that are more susceptible to classification errors. The experimental results indicate that the Meta-CTX can efficiently combine noise smoothing with the preservation of small spatial details in remote sensing image classification. Area SRE Arrangement 1 urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > CAP > A meta-methodology for... Arrangement 2 urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > A meta-methodology for... Arrangement 3 urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > A meta-methodology for... doc Directory Content there are no files source Directory Content there are no files agreement Directory Content agreement.html 12/12/2022 15:32 1.0 KiB  4. Conditions of access and use Language en User Group simone Reader Group administrator simone Visibility shown Archiving Policy denypublisher denyfinaldraft12 Read Permission deny from all and allow from 150.163 Update Permission not transferred 5. Allied materials Next Higher Units 8JMKD3MGPCW/3F2PHGS 8JMKD3MGPCW/46KUATE 8JMKD3MGPCW/46KUES5 Dissemination WEBSCI; PORTALCAPES; COMPENDEX; SCOPUS. Host Collection urlib.net/www/2021/06.04.03.40 6. Notes Empty Fields alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn keywords label lineage mark mirrorrepository month nextedition notes numberoffiles orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle size sponsor subject targetfile tertiarymark tertiarytype url 7. Description control e-Mail (login) simone update

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