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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKB5R7W/3D4C4JE
Repositoryurlib.net/www/2012/11.24.11.08   (restricted access)
Last Update2012:11.24.11.08.46 (UTC) administrator
Metadata Repositoryurlib.net/www/2012/11.24.11.08.51
Metadata Last Update2018:06.05.04.15.18 (UTC) administrator
DOI10.1111/j.1740-8261.2011.01909.x
ISSN1058-8183
Citation KeyBanonFoBaBoGoPe:2012:CoAuAp
TitleComparison between automatic approach of the vertebral heart size and normalized cardiac area to assess left atrial enlargement in poodles with mitral insufficiency
Year2012
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size646 KiB
2. Context
Author1 Banon, Gabriela Paola Ribeiro
2 Fonseca Pinto, Ana Carolina Brandão de Campos
3 Banon, Gerald Jean Francis
4 Boroni, Carina Outi
5 Goldfeder, Guilherme Teixeira
6 Pelegrino, Arine
Resume Identifier1 8JMKD3MGP5W/3G98J55
2
3 8JMKD3MGP5W/3C9EMTE
Group1
2
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1
2
3 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalVeterinary Radiology and Ultrasound
Volume53
Number1
Pages105
History (UTC)2012-11-25 18:36:05 :: banon -> administrator :: 2012
2018-06-05 04:15:18 :: administrator -> :: 2012
3. Content and structure
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Content TypeExternal Contribution
Keywordscoração
vhs
acn
radiografia
poodle
cães
mensuração
radiograph
dog
measurement
left atrial enlargement
mitral insufficiency
radiology
heart
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Comparison between automatic...
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Target FileVetewrinaryRadiologyAndUlrasound.pdf
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Previous Editionsid.inpe.br/mtc-m19/2012/01.06.16.28
Mirror Repositorydpi.inpe.br/banon/1999/06.19.17.00
Next Higher Units8JMKD3MGPCW/3EQCCU5
DisseminationWEBSCI; PORTALCAPES.
Host Collectiondpi.inpe.br/banon/1999/01.09.22.14
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6. Notes
NotesAbstract from the Annual Meeting of the American College of Veterinary Radiology. Albuquerque, NM. October 11-14, 2011
Empty Fieldsabstract alternatejournal archivist area callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark month nextedition orcid parameterlist parentrepositories previouslowerunit progress project readergroup rightsholder secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url versiontype

1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3DBG7J8
Repositorysid.inpe.br/mtc-m19/2013/01.07.10.58   (restricted access)
Last Update2013:01.07.11.05.07 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2013/01.07.10.58.03
Metadata Last Update2018:06.05.04.13.38 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1068/b38117
ISSN0265-8135
1472-3417
Citation KeyFeitosaLeVleMonRos:2012:PoExUs
TitleCountering urban segregation in Brazilian cities: policy-oriented explorations using agent-based simulation
Year2012
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size4170 KiB
2. Context
Author1 Feitosa, Flávia F
2 Le, Quang Bao
3 Vlek, Paul L G
4 Monteiro, Antônio Miguel V
5 Rosemback, Roberta
Group1
2
3
4 DPI-OBT-INPE-MCTI-GOV-BR
5 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, D-53113, Bonn, Germany
2 Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, D-53113, Bonn, Germany
3 Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, D-53113, Bonn, Germany
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalEnvironment and Planning B: Planning and Design
Volume39
Number6
Pages1131 – 1150
History (UTC)2013-01-07 11:14:17 :: marciana -> administrator :: 2012
2018-06-05 04:13:38 :: administrator -> marciana :: 2012
3. Content and structure
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Keywordsurban segregation
social mix
urban policies
social simulation
agent-based model
Brazil
AbstractIn this study we use agent-based simulations to explore the impact of social-mix policies on the segregation dynamics of São José dos Campos, a medium-sized Brazilian city. We use the model MASUS, Multi-Agent Simulator for Urban Segregation, to test two policy strategies: one based on the spatial dispersal of poverty, and the other on the spatial dispersal of wealth. The experiments indicated that these strategies reveal varying shortcomings and complementary benefits in cities such as São José dos Campos. While poverty dispersal provides immediate results on segregation levels and direct benefits for the assisted families, wealth dispersal can produce long-term outcomes and promote a positive change in the overall levels and patterns of segregation in the city.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Countering urban segregation...
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Mirror Repositoryiconet.com.br/banon/2006/11.26.21.31
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DisseminationWEBSCI
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6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark month nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder secondarydate secondarymark session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CEPTG5
Repositorysid.inpe.br/mtc-m19/2012/08.14.14.20   (restricted access)
Last Update2012:08.17.18.19.32 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/08.14.14.20.13
Metadata Last Update2018:06.05.04.12.41 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1016/j.cageo.2011.11.020
ISSN0098-3004
Citation KeyCruzMontSant:2012:AuGeWe
TitleAutomated geospatial web services composition based on geodata quality requirements
Year2012
MonthOct.
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size1955 KiB
2. Context
Author1 Cruz, Sérgio A. B.
2 Monteiro, Antonio M. V.
3 Santos, Rafael Duarte Coelho dos
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JJ4N
Group1
2 DPI-OBT-INPE-MCTI-GOV-BR
3 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1 Embrapa Agriculture Informatics, Embrapa
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalComputers and Geosciences
Volume47
Pages60–74
Secondary MarkA2_CIÊNCIA_DA_COMPUTAÇÃO B4_CIÊNCIAS_BIOLÓGICAS_II B1_ENGENHARIAS_I B1_GEOCIÊNCIAS A2_INTERDISCIPLINAR
History (UTC)2012-08-14 14:20:13 :: marciana -> administrator ::
2012-08-14 14:20:14 :: administrator -> marciana :: 2011
2012-08-14 14:26:39 :: marciana :: 2011 -> 2012
2012-12-27 14:58:24 :: marciana -> administrator :: 2012
2018-06-05 04:12:41 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsService-Oriented Architecture (SOA)
Geoprocessing
Web Service composition
Geospatial analysis
Geodata quality
Artificial intelligence planning method
AbstractService-Oriented Architecture and Web Services technologies improve the performance of activities involved in geospatial analysis with a distributed computing architecture. However, the design of the geospatial analysis process on this platform, by combining component Web Services, presents some open issues. The automated construction of these compositions represents an important research topic. Some approaches to solving this problem are based on AI planning methods coupled with semantic service descriptions. This work presents a new approach using AI planning methods to improve the robustness of the produced geospatial Web Services composition. For this purpose, we use semantic descriptions of geospatial data quality requirements in a rule-based form. These rules allow the semantic annotation of geospatial data and, coupled with the conditional planning method, this approach represents more precisely the situations of nonconformities with geodata quality that may occur during the execution of the Web Service composition. The service compositions produced by this method are more robust, thus improving process reliability when working with a composition of chained geospatial Web Services.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Automated geospatial web...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Automated geospatial web...
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8JMKD3MGPCW/3ESGTTP
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate session shorttitle sponsor subject targetfile tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CBDEPS
Repositorysid.inpe.br/mtc-m19/2012/07.24.18.13   (restricted access)
Last Update2012:08.20.11.43.30 (UTC) marciana
Metadata Repositorysid.inpe.br/mtc-m19/2012/07.24.18.13.09
Metadata Last Update2018:06.05.04.12.32 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1098/rsbl.2011.0942
ISSN1744-9561
Citation KeyKaminoSAMRSGH:2012:ChPeSp
TitleChallenges and perspectives for species distribution modelling in the neotropics
Year2012
MonthJune
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size116 KiB
2. Context
Author1 Kamino, Luciana H. Y
2 Stehmann, Joao Renato
3 Amaral, Silvana
4 Marco Jr., Paulo
5 Rangel, Thiago F.
6 de Siqueira, Marinez F.
7 De Giovanni, Renato
8 Hortal, Joaquin
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JJ8Q
Group1
2
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Univ Fed Minas Gerais, Inst Ciencias Biol, Depto Bot, Belo Horizonte, MG, Brazil.
2 Univ Fed Minas Gerais, Inst Ciencias Biol, Depto Bot, Belo Horizonte, MG, Brazil.
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Univ Fed Goias, Depto Ecol, Inst Ciencias Biol, Goiania, Go, Brazil.
5 Univ Fed Goias, Depto Ecol, Inst Ciencias Biol, Goiania, Go, Brazil.
6 Inst Pesquisa Jardim Bot Rio de Janeiro, Unidade Bot Sistemat, Rio De Janeiro, Brazil
7 Ctr Referencia Informacao Ambiental, Campinas, SP, Brazil
8 Museo Nacl Ciencias Nat CSIC, Depto Biodiversidad & Biol Evolut, Madrid, Spain
JournalBiology Letters
Volume8
Number3
Pages324 - 326
History (UTC)2012-08-20 11:43:30 :: marciana -> administrator :: 2012
2012-11-16 17:03:33 :: administrator -> marciana :: 2012
2012-11-29 13:16:54 :: marciana -> administrator :: 2012
2018-06-05 04:12:32 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsWallacean shortfall
rare species
habitat suitability mapping
biodiversity databases
bias
experimental design
AbstractThe workshop 'Species distribution models: applications, challenges and perspectives' held at Belo Horizonte (Brazil), 29-30 August 2011, aimed to review the state-of-the-art in species distribution modelling (SDM) in the neotropical realm. It brought together researchers in ecology, evolution, biogeography and conservation, with different backgrounds and research interests. The application of SDM in the megadiverse neotropics-where data on species occurrences are scarce-presents several challenges, involving acknowledging the limitations imposed by data quality, including surveys as an integral part of SDM studies, and designing the analyses in accordance with the question investigated. Specific solutions were discussed, and a code of good practice in SDM studies and related field surveys was drafted.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Challenges and perspectives...
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Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3EQCCU5
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarymark session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3B5HGMP
Repositorysid.inpe.br/mtc-m19/2012/01.04.16.56   (restricted access)
Last Update2012:08.14.13.44.38 (UTC) marciana
Metadata Repositorysid.inpe.br/mtc-m19/2012/01.04.16.56.34
Metadata Last Update2018:06.05.04.12.17 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1016/j.apgeog.2011.04.003
ISSN0143-6228
Citation KeyEspindolaAguPebCamFon:2012:AgLaUs
TitleAgricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data
Year2012
MonthMar.
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size1505 KiB
2. Context
Author1 Espindola, Giovana Mira de
2 Aguiar, Ana Paula Dutra de
3 Pebesma, E.
4 Camâra, Gilberto
5 Fonseca, L.
Resume Identifier1
2 8JMKD3MGP5W/3C9JGHD
3
4 8JMKD3MGP5W/3C9JHB8
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2 CST-CST-INPE-MCTI-GOV-BR
3
4 CST-CST-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3
4 Instituto Nacional de Pesquisas Espaciais (INPE)
e-Mail Addresssecretaria.cpa@dir.inpe.br
JournalApplied Geography
Volume32
Number2
Pages240-252
Secondary MarkA2_ECOLOGIA_E_MEIO_AMBIENTE A1_ENGENHARIAS_III A1_GEOGRAFIA A1_INTERDISCIPLINAR
History (UTC)2012-01-17 14:18:34 :: secretaria.cpa@dir.inpe.br :: 2011 -> 2012
2012-02-07 16:20:03 :: secretaria.cpa@dir.inpe.br -> banon :: 2012
2012-02-07 16:22:18 :: banon -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-14 13:44:38 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2016-06-04 23:17:14 :: administrator -> marciana :: 2012
2016-10-14 18:32:04 :: marciana -> administrator :: 2012
2018-06-05 04:12:17 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsBrazilian Amazon
Deforestation
Land use dynamic
Agricultural land uses
Spatial regression analysis
AbstractThe potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the atmosphere calls for policies that take account of changes in forest cover. Although much research has focused on the location and effects of deforestation, little is known about the distribution and reasons for the agricultural uses that replace forest cover. We used Landsat TM-based deforestation and agricultural census data to generate maps of the distribution and proportion of four major agricultural land uses throughout the Brazilian Amazon in 1997 and 2007. We built linear and spatial regression models to assess the determinant factors of deforestation and those major agricultural land uses e pasture, temporary agriculture and permanent agriculture e for the states of Pará, Rondônia, and Mato Grosso. The data include 30 determinant factors that were grouped into two years (1996 and 2006) and in four categories: accessibility to markets, public policies, agrarian structure, and environment. We found an overall expansion of the total agricultural area between 1997 and 2007, and notable differences between the states of Pará, Rondônia, and Mato Grosso in land use changes during this period. Regression models for deforestation and pasture indicated that determinant factors such as distance to roads were more influential in 1997 than in 2007. The number of settled families played an important role in the deforestation and pasture, the effect was stronger in 2007 than 1997. Indigenous lands were significant in preventing deforestation in high-pressure areas in 2007. For temporary and permanent agricultures, our results show that in 1997 the effect of small farms was stronger than in 2007. The mapped land use time series and the models explain empirically the effects of land use changes across the region over one decade.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Agricultural land use...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > COCST > Agricultural land use...
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Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3F3T29H
DisseminationWEBSCI; PORTALCAPES.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel electronicmailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate session shorttitle sponsor subject targetfile tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3D545NA
Repositorydpi.inpe.br/plutao/2012/11.28.19.14.50
Last Update2013:03.21.17.02.44 (UTC) marciana
Metadata Repositorydpi.inpe.br/plutao/2012/11.28.19.14.51
Metadata Last Update2018:06.05.00.02.13 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1007/978-3-642-33275-3_98
ISSN0302-9743
Labellattes: 9840759640842299 2 NegriDutrSant:2012:StApMi
Citation KeyNegriDutrSant:2012:StApMi
TitleStochastic Approaches of Minimum Distance Method for Region Based Classification
Year2012
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size2090 KiB
2. Context
Author1 Negri, Rogerio Galanti
2 Dutra, Luciano Vieira
3 Sant'Anna, Sidnei JoÃo Siqueira
Resume Identifier1
2 8JMKD3MGP5W/3C9JHMA
3 8JMKD3MGP5W/3C9JJ8N
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DPI-OBT-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
2 dutra@dpi.inpe.br
e-Mail Addressdutra@dpi.inpe.br
JournalLecture Notes in Computer Science
Volume7441
Number2012
Pages797-804
Secondary MarkC_ADMINISTRAÇÃO,_CIÊNCIAS_CONTÁBEIS_E_TURISMO C_ASTRONOMIA_/_FÍSICA C_BIOTECNOLOGIA B5_CIÊNCIAS_BIOLÓGICAS_I C_CIÊNCIAS_BIOLÓGICAS_III B1_CIÊNCIAS_SOCIAIS_APLICADAS_I B3_DIREITO C_EDUCAÇÃO C_ENGENHARIAS_I B3_ENGENHARIAS_II C_ENGENHARIAS_III B4_ENSINO_DE_CIÊNCIAS_E_MATEMATICA B5_GEOCIÊNCIAS B2_INTERDISCIPLINAR B5_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B3_MEDICINA_I B3_MEDICINA_II B3_PSICOLOGIA
History (UTC)2012-11-28 23:06:35 :: lattes -> administrator :: 2012
2012-11-29 13:42:22 :: administrator -> marciana :: 2012
2012-12-03 12:48:46 :: marciana -> administrator :: 2012
2013-01-20 15:55:31 :: administrator -> marciana :: 2012
2013-03-21 17:02:45 :: marciana -> administrator :: 2012
2018-06-05 00:02:13 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typefinaldraft
KeywordsClassification process
Image simulations
Minimum average distance
Minimum distance
Region-based
Remote sensing image classification
Second variation
Simple approach
Simulation studies
Stochastic approach
stochastic distances
Imagens de Sensoriamento Remoto
Reconhecimento de Padroes
Segmentação de imagens
AbstractNormally remote sensing image classification is performed pixelwise which produces a noisy classification. One way of improving such results is dividing the classification process in two steps. First, uniform regions by some criterion are detected and afterwards each unlabeled region is assigned to class of the "nearest" class using a so-called stochastic distance. The statistics are estimated by taking in account all the reference pixels. Three variations are investigated. The first variation is to assign to the unlabeled region a class that has the minimum average distance between this region and each one of reference samples of that class. The second is to assign the class of the closest reference sample. The third is to assign the most frequent class of the k closest reference regions. A simulation study is done to assess the performances. The simulations suggested that the most robust and simple approach is the second variation.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Stochastic Approaches of...
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data URLhttp://urlib.net/ibi/J8LNKAN8RW/3D545NA
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3D545NA
Languageen
Target FilePaper-PublishedVersion-74410797.pdf
User Groupadministrator
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marciana
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5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Notes17th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2012
Buenos Aires
3 September 2012through6 September 2012
Code92323
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository month nextedition orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission rightsholder secondarydate session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
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Reference TypeJournal Article
Siteplutao.sid.inpe.br
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Repositorydpi.inpe.br/plutao/2012/06.21.21.48   (restricted access)
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DOI10.1016/j.isprsjprs.2012.03.010
ISSN0924-2716
1872-8235
Labellattes: 9840759640842299 4 LiLuMorDutBat:2012:CoAnAL
Citation KeyLiLuMorDutBat:2012:CoAnAL
TitleA comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region
Year2012
MonthJune
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size2059 KiB
2. Context
Author1 Li, Guiying
2 Lu, Dengsheng
3 Moran, Emilio
4 Dutra, Luciano Vieira
5 Batistella, Mateus
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JHMA
Group1
2
3
4 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Bloomington, Indiana 47405
2 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Bloomington, Indiana 47405
3 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Bloomington, Indiana 47405
4 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4 dutra@dpi.inpe.br
e-Mail Addressdutra@dpi.inpe.br
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume70
Pages26-38
Secondary MarkA1_CIÊNCIAS_AGRÁRIAS_I A2_ECOLOGIA_E_MEIO_AMBIENTE B1_ENGENHARIAS_IV A2_GEOCIÊNCIAS A1_INTERDISCIPLINAR
History (UTC)2012-06-22 00:11:01 :: lattes -> administrator :: 2012
2012-07-26 23:15:52 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-14 14:06:11 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:51 :: administrator -> marciana :: 2012
3. Content and structure
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Content TypeExternal Contribution
Version Typepublisher
KeywordsALOS PALSAR
RADARSAT
Texture
Land-cover classification
Amazon
AbstractThis paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
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6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C64845
Repositorydpi.inpe.br/plutao/2012/06.21.21.32   (restricted access)
Last Update2012:08.10.14.07.01 (UTC) marciana
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.21.32.41
Metadata Last Update2018:06.05.00.01.50 (UTC) administrator
DOI10.1016/j.actatropica.2011.10.011
ISSN0001-706X
1873-6254
Labellattes: 9840759640842299 3 ScholteFrDuGuDrOlCa:2012:UtEnSo
Citation KeyScholteFrDuGuDrOlCa:2012:UtEnSo
TitleUtilizing environmental, socioeconomic data and GIS techniques to estimate the risk for ascariasis and trichuriasis in Minas Gerais, Brazil
Year2012
MonthFeb.
Access Date2023, Jan. 31
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1505 KiB
2. Context
Author1 Scholte, Ronaldo G. C.
2 Freitas, Corina da Costa
3 Dutra, Luciano Vieira
4 Guimaraes, Ricardo J. P. S.
5 Drummond, Sandra C.
6 Oliveira, Guilherme
7 Carvalho, Omar S.
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHMA
Group1
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland;
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4
5 Health State Office of Minas Gerais, Belo Horizonte, MG, Brazil
6
7 Research Center René Rachou/Fiocruz-MG, Av. Augusto de Lima, 1715 Barro Preto, CEP 30190-002 Belo Horizonte, MG, Brazil
Author e-Mail Address1 ronaldo.scholte@unibas.ch
2 corina@dpi.inpe.br
3 dutra@dpi.inpe.br
e-Mail Addressdutra@dpi.inpe.br
JournalActa Tropica
Volume121
Number2
Pages112-117
Secondary MarkB2_ASTRONOMIA_/_FÍSICA B2_BIOTECNOLOGIA A2_CIÊNCIAS_BIOLÓGICAS_I B2_CIÊNCIAS_BIOLÓGICAS_II B2_CIÊNCIAS_BIOLÓGICAS_III A2_ENFERMAGEM A2_ENGENHARIAS_II C_ENSINO_DE_CIÊNCIAS_E_MATEMATICA B1_FARMÁCIA A2_GEOCIÊNCIAS B3_GEOGRAFIA A2_INTERDISCIPLINAR B1_MEDICINA_I B1_MEDICINA_II B1_MEDICINA_III B1_MEDICINA_VETERINÁRIA B1_QUÍMICA B1_SAÚDE_COLETIVA A2_ZOOTECNIA_/_RECURSOS_PESQUEIROS
History (UTC)2012-06-22 00:11:01 :: lattes -> administrator :: 2012
2012-07-26 23:24:59 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-14 17:37:01 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2012-10-07 01:52:42 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-12-12 18:40:01 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2013-01-20 15:54:01 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2013-02-07 11:51:16 :: secretaria.cpa@dir.inpe.br -> banon :: 2012
2013-02-18 14:16:50 :: banon -> administrator :: 2012
2016-06-04 01:08:05 :: administrator -> marciana :: 2012
2016-08-29 18:53:10 :: marciana -> administrator :: 2012
2018-06-05 00:01:50 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
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Version Typepublisher
Keywordsepidemiologia
Endemic diseases
sensoriamento remoto
Remote Sensing
AbstractThe impact of intestinal helminths on human health is well known among the population and health authorities because of their wide geographic distribution and the serious problems they cause. Geohelminths are highly prevalent and have a big impact on public health, mainly in underdeveloped and developing countries. Geohelminths are responsible for the high levels of debility found in the younger population and are often related to cases of chronic diarrhea and malnutrition, which put the physical and intellectual development of children at risk. These geohelminths have not been sufficiently studied. One obstacle in implementing a control program is the lack of knowledge of the prevalence and geographical distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized to improve understanding of infectious disease distribution and climatic patterns. In this study, GIS and RS technologies, as well as meteorological, social, and environmental variables were utilized for the modeling and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were those produced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factors related to the transmission of ascariasis and trichuriasis and confirmed the key association between environmental variables and the poverty index, which enabled us to identify priority areas for intervention planning in the state of Minas Gerais in Brazil.
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Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Utilizing environmental, socioeconomic...
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DisseminationWEBSCI; PORTALCAPES.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesSetores de Atividade: Pesquisa e desenvolvimento científico, Saúde humana e serviços sociais, Produção Florestal.
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository nextedition orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C644GH
Repositorydpi.inpe.br/plutao/2012/06.21.20.49
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Metadata Repositorydpi.inpe.br/plutao/2012/06.21.20.49.34
Metadata Last Update2018:06.05.00.01.49 (UTC) administrator
DOI10.3390/rs4030745
ISSN2072-4292
Labellattes: 8981152353948408 1 MuraPinhRosaMore:2012:PhEsMe
Citation KeyMuraPinhRosaMore:2012:PhEsMe
TitleA Phase-Offset Estimation Method for InSAR DEM Generation Based on Phase-Offset Functions
Year2012
MonthMar.
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size2102 KiB
2. Context
Author1 Mura, Jose Claudio
2 Pinheiro, Muriel
3 Rosa, Rafael
4 Moreira, João Roberto
Resume Identifier1 8JMKD3MGP5W/3C9JHGR
Group1 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 mura@dpi.inpe.br
2 murielaline86@gmail.com
3 rafael@orbisat.com.br
4 joao.moreira@orbisat.com.br
e-Mail Addressmura@dpi.inpe.br
JournalRemote Sensing
Volume4
Number3
Pages745-761
History (UTC)2012-06-22 00:11:01 :: lattes -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-14 19:34:04 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2012-10-19 20:27:02 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-12-12 18:04:05 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:49 :: administrator -> marciana :: 2012
3. Content and structure
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KeywordsSAR Interferometry
phase offset estimation
absolute phase
DEM
AbstractThis paper presents a novel method for estimating the absolute phase offset in interferometric synthetic aperture radar (SAR) measurements for digital elevation model (DEM) generation. The method is based on phase-offset functions (POF), relating phase offset to topographic height, and are computed for two different overlapping interferometric data acquisitions performed with considerably different incidence angles over the same area of interest. For the purpose of extended mapping, opposite viewing directions are preferred. The two phase-offset functions are then linearly combined, yielding a combined phase-offset function (CPOF). The intersection point of several straight lines (CPOFs), corresponding to different points in the overlap area allows for solving the phase offset for both acquisitions. Aiming at increasing performance and stability, this intersection point is found by means of averaging many points and applying principal component analysis. The method is validated against traditional phase offset estimation with corner reflectors (CR) using real OrbiSAR-1 data in X- and P-band.
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data URLhttp://urlib.net/ibi/J8LNKAN8RW/3C644GH
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3C644GH
Languageen
Target FileMura_et_al_2012.pdf
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DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C63QEE
Repositorydpi.inpe.br/plutao/2012/06.21.19.23   (restricted access)
Last Update2012:08.10.12.43.11 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.19.23.57
Metadata Last Update2018:06.05.00.01.46 (UTC) administrator
DOI10.1080/01431161.2012.675451
ISSN0143-1161
Labellattes: 3233696672067020 5 PinhoFonKorAlmKux:2012:LaClIn
Citation KeyPinhoFonKörAlmKux:2012:LaClIn
TitleLand-cover classification of an intra-urban environment using high-resolution images and object-based image analysis
Year2012
MonthOct.
Access Date2023, Jan. 31
Secondary TypePRE PI
Number of Files1
Size3073 KiB
2. Context
Author1 Pinho, Carolina Moutinho Duque
2 Fonseca, Leila Maria Garcia
3 Körting, Thales Sehn
4 Almeida, Cláudia Maria de
5 Kux, Hermann Johann Heinrich
Resume Identifier1
2 8JMKD3MGP5W/3C9JHLD
3
4 8JMKD3MGP5W/3C9JGS3
5 8JMKD3MGP5W/3C9JHCD
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DPI-OBT-INPE-MCTI-GOV-BR
4 DSR-OBT-INPE-MCTI-GOV-BR
5 DSR-OBT-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)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4 almeida@dsr.inpe.br
5 hermann@ltid.inpe.br
e-Mail Addresshermann@ltid.inpe.br
JournalInternational Journal of Remote Sensing
Volume33
Number19
Pages5973-5995
Secondary MarkB3_BIOTECNOLOGIA A1_CIÊNCIA_DA_COMPUTAÇÃO A2_CIÊNCIAS_AGRÁRIAS_I B2_CIÊNCIAS_BIOLÓGICAS_I B1_ECOLOGIA_E_MEIO_AMBIENTE B1_ENGENHARIAS_I B2_ENGENHARIAS_II B1_ENGENHARIAS_III A2_ENGENHARIAS_IV B1_GEOCIÊNCIAS A1_GEOGRAFIA A2_INTERDISCIPLINAR B1_ODONTOLOGIA A1_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_SAÚDE_COLETIVA
History (UTC)2012-06-22 00:11:00 :: lattes -> administrator :: 2012
2012-07-19 18:01:58 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-08-10 12:43:11 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2012-10-06 09:16:07 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2013-01-18 14:31:20 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:46 :: administrator -> marciana :: 2012
3. Content and structure
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Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsAnálise de imagens orientada a objeto - OBIA
Classificação de imagens baseada em conhecimento
IKONOS
QUICKBIRD
Planejamento Urbano
São José dos Campos-SP
AbstractDetailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off estimates and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from the satellites IKONOS II, Quickbird, Orbview and WorldView II, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and resolution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land-cover mapping based on an OBIA approach using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km2 with 50 neighbourhoods), which is located in São Paulo State in south-eastern Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Land-cover classification of...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Land-cover classification of...
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Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesInformações Adicionais: Detailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from IKONOS II, Quickbird-2, OrbView and WorldView-2, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and solution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land cover mapping based on an OBIA approach, using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km2 with 50 neighbourhoods), which is located in São Paulo State, in SE Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%..
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