1. Identificação | |
Tipo de Referência | Artigo em Evento (Conference Proceedings) |
Site | mtc-m21d.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34T/454U5F5 |
Repositório | sid.inpe.br/mtc-m21d/2021/07.19.13.08 |
Última Atualização | 2021:07.19.13.08.31 (UTC) simone |
Repositório de Metadados | sid.inpe.br/mtc-m21d/2021/07.19.13.08.31 |
Última Atualização dos Metadados | 2022:04.03.22.28.51 (UTC) administrator |
Chave Secundária | INPE--PRE/ |
ISBN | 978-1-61208-871-6 |
ISSN | 2308-393X |
Chave de Citação | PachecoMaSiSoShEs:2021:ImClMe |
Título | Image Classification Methods Assessment for Identification of Small-Scale Agriculture in Brazilian Amazon |
Ano | 2021 |
Data de Acesso | 03 maio 2024 |
Tipo Secundário | PRE CI |
Número de Arquivos | 1 |
Tamanho | 1032 KiB |
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2. Contextualização | |
Autor | 1 Pacheco, Flávia Domingos 2 Matias, Maíra Ramalho 3 Silva, Gabriel Máximo da 4 Souza, Anielli Rosane de 5 Shimabukuro, Yosio Edemir 6 Escada, Maria Isabel Sobral |
Identificador de Curriculo | 1 2 3 4 5 8JMKD3MGP5W/3C9JJCQ 6 8JMKD3MGP5W/3C9JHRG |
Grupo | 1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 2 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 3 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 4 SER-SRE-DIPGR-INPE-MCTI-GOV-BR 5 DIOTG-CGCT-INPE-MCTI-GOV-BR 6 DIOTG-CGCT-INPE-MCTI-GOV-BR |
Afiliação | 1 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) 6 Instituto Nacional de Pesquisas Espaciais (INPE) |
Endereço de e-Mail do Autor | 1 flavia.pacheco@inpe.br 2 mairamatias.geo@gmail.com 3 gabrielmaximo04@gmail.com 4 aniellirosane@yahoo.com.br 5 edemirshima@gmail.com 6 isabel.escada@inpe.br |
Nome do Evento | International Conference on Advanced Geographic Information Systems, Applications, and Services, 13 (GEOProcessing) |
Localização do Evento | Nice, France |
Data | 19-22 july |
Editora (Publisher) | IARIA |
Páginas | 12-19 |
Título do Livro | Proceedings |
Histórico (UTC) | 2021-07-19 13:09:05 :: simone -> administrator :: 2021 2022-04-03 22:28:51 :: administrator -> simone :: 2021 |
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3. Conteúdo e estrutura | |
É a matriz ou uma cópia? | é a matriz |
Estágio do Conteúdo | concluido |
Transferível | 1 |
Tipo de Versão | publisher |
Palavras-Chave | digital image processing segmentation land use land cover smallholders planetscope |
Resumo | This paper aims to test different methods for image classification focusing on small-scale agriculture in the region of Mocajuba and Cametá, municipalities in the Northeast of Pará state, Brazil. It is an important land use class, always ignored by Land-Use and Land-Cover monitoring systems because of its small size and variable spectral signature. We used an image from the PlanetScope Surface Reflectance Mosaics (Analysis Ready) with spatial resolution of 4.77 meters and 4 spectral bands (red, green, blue and infra-red). After proceeding with a multiresolution segmentation to identify image objects, two object-oriented classification algorithms were tested: Adapted Nearest-neighbor and C5.0 Decision trees algorithms. We selected 122 random points using the images available on Google Earth Pro as reference to assess the accuracy of classifications. Afterwards, confusion matrices were generated. Both methods showed similar overall accuracy and kappa value. However, C5.0 Decision trees reached a higher producers accuracy to small-scale agriculture (75%) in comparison to Adapted Nearest-neighbor (65%). The average size of the small-scale agriculture segments estimated was less than 1 ha in both maps, showing the need to carry out studies on scales of greater detail, preferably with images of high spatial resolution to represent these systems properly. In this study, C5.0 Decision trees had the best result, representing the most suitable method for mapping small-scale agriculture in Brazilian Amazon. |
Área | SRE |
Arranjo | Image Classification Methods... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
URL dos dados | http://urlib.net/ibi/8JMKD3MGP3W34T/454U5F5 |
URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W34T/454U5F5 |
Idioma | en |
Arquivo Alvo | geoprocessing_2021_1_40_30034.pdf |
Grupo de Usuários | simone |
Visibilidade | shown |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPCW/46KUATE |
Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 |
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6. Notas | |
Campos Vazios | archivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress readergroup readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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