| 1. Identificação | |
| Tipo de Referência | Artigo em Evento (Conference Proceedings) |
| Site | mtc-m16c.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP8W/3BTBAG8 |
| Repositório | sid.inpe.br/mtc-m18/2012/05.17.14.34 |
| Última Atualização | 2012:05.17.14.34.45 (UTC) administrator |
| Repositório de Metadados | sid.inpe.br/mtc-m18/2012/05.17.14.34.45 |
| Última Atualização dos Metadados | 2018:06.04.03.55.40 (UTC) administrator |
| ISBN | 978-85-17-00059-1 |
| Chave de Citação | LeonardiAlmFonTomOli:2012:GeAlDa |
| Título | Genetic algorithms and data mining applied to optical orbital and LiDAR data for object-based classification of urban land cover  |
| Formato | On-line. |
| Ano | 2012 |
| Data de Acesso | 12 maio 2024 |
| Tipo Secundário | PRE CI |
| Número de Arquivos | 1 |
| Tamanho | 1778 KiB |
| 2. Contextualização | |
| Autor | 1 Leonardi, Fernando 2 Almeida, Claudia Maria 3 Fonseca, Leila Maria Garcia 4 Tomas, Livia 5 Oliveira, Cleber |
| Identificador de Curriculo | 1 2 3 8JMKD3MGP5W/3C9JHLD |
| Grupo | 1 2 DSR-OBT-INPE-MCTI-GOV-BR 3 DPI-OBT-INPE-MCTI-GOV-BR |
| Afiliação | 1 2 undefined 3 undefined |
| Endereço de e-Mail do Autor | 1 fernando@geopx.com.br 2 almeida@dsr.inpe.br 3 leila@dpi.inpe.br 4 liviatomas@gmail.com 5 cleber@dsr.inpe.br |
| Editor | Feitosa, Raul Queiroz Costa, Gilson Alexandre Ostwald Pedro da Almeida, Cláudia Maria de Fonseca, Leila Maria Garcia Kux, Hermann Johann Heinrich |
| Endereço de e-Mail | wanderf@dsr.inpe.br |
| Nome do Evento | International Conference on Geographic Object-Based Image Analysis, 4 (GEOBIA). |
| Localização do Evento | Rio de Janeiro |
| Data | May 7-9, 2012 |
| Editora (Publisher) | Instituto Nacional de Pesquisas Espaciais (INPE) |
| Cidade da Editora | São José dos Campos |
| Páginas | 649-654 |
| Título do Livro | Proceedings |
| Organização | Instituto Nacional de Pesquisas Espaciais (INPE) |
| Histórico (UTC) | 2012-05-17 14:34:45 :: wanderf@dsr.inpe.br -> administrator :: 2012-05-30 13:44:48 :: administrator -> wanderf@dsr.inpe.br :: 2012 2012-06-01 15:12:44 :: wanderf@dsr.inpe.br -> marciana :: 2012 2012-06-12 14:28:25 :: marciana -> seki@dsr.inpe.br :: 2012 2012-06-13 15:55:31 :: seki@dsr.inpe.br -> marciana :: 2012 2012-06-14 15:03:56 :: marciana -> administrator :: 2012 2018-06-04 03:55:40 :: administrator -> :: 2012 |
| 3. Conteúdo e estrutura | |
| É a matriz ou uma cópia? | é a matriz |
| Estágio do Conteúdo | concluido |
| Transferível | 1 |
| Palavras-Chave | Laser Scanning Decision Tree Semantic Network Semi-Automated Classification |
| Resumo | The study of the urban environment has raised great interest among researchers and practitioners involved with the use of remote sensing, in face of the challenges for its investigation and the complexity of its targets. Although they have great potential for studies of urban environments, the high-resolution images present difficulties for automatic extraction of information because they are characterized by high spatial and spectral heterogeneity for the same segment, which greatly complicates segmentation and classification processes. Thus, new concepts and analyses have been used for mapping the urban space. Object-based image analysis and multiresolution segmentation have been quite efficient in the discrimination of urban targets in high spatial resolution images. One technique that can assist the classification process is data mining, which can be used to explore large data sets, identify and characterize patterns of interest, and hence, support the precise extraction of useful information. In this context, this paper proposes a methodology jointly employing cognitive approaches (semantic net, object-based image analysis) and data mining (genetic algorithms and decision trees) for the classification of urban land cover from optical orbital and airborne laser data. To assess the efficacy of the methodology and ensure the accuracy of the produced maps, the steps undertaken in this study were subject to quality control. The results were presented and discussed, indicating a satisfactory accuracy in the generated mapping products, demonstrating the reliability of the methodology for mapping land cover in urban areas. |
| Área | SRE |
| Tipo | LiDAR and SAR Applications |
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Genetic algorithms and... |
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Genetic algorithms and... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| URL dos dados | http://urlib.net/ibi/8JMKD3MGP8W/3BTBAG8 |
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP8W/3BTBAG8 |
| Idioma | en |
| Arquivo Alvo | 179.pdf |
| Grupo de Usuários | administrator marciana wanderf@dsr.inpe.br |
| Visibilidade | shown |
| 5. Fontes relacionadas | |
| Repositório Espelho | urlib.net/www/2011/03.29.20.55 |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3ER446E |
| Acervo Hospedeiro | sid.inpe.br/mtc-m18@80/2008/03.17.15.17 |
| 6. Notas | |
| Campos Vazios | archivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition issn label lineage mark nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor shorttitle sponsor tertiarymark tertiarytype url versiontype volume |