| 1. Identificação | ||||
| Tipo de Referência | Artigo em Evento (Conference Proceedings) | |||
| Site | mtc-m21b.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34P/49NADP5 | |||
| Repositório | sid.inpe.br/mtc-m21b/2023/08.29.12.50 | |||
| Última Atualização | 2023:08.29.12.50.09 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21b/2023/08.29.12.50.09 | |||
| Última Atualização dos Metadados | 2023:09.26.02.57.55 (UTC) administrator | |||
| Chave Secundária | INPE--PRE/ | |||
| Chave de Citação | MarettoFonsKört:2017:DeLeTe | |||
| Título | Deep Learning Techniques Applied to classification of Remote Sensing Images  | |||
| Ano | 2017 | |||
| Data de Acesso | 11 maio 2024 | |||
| Tipo Secundário | PRE CN | |||
| Número de Arquivos | 1 | |||
| Tamanho | 182 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Maretto, Raian Vargas 2 Fonseca, Leila Maria Garcia 3 Körting, Thales Sehn | |||
| Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JHLD | |||
| Grupo | 1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR 2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR 3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 raian@dpi.inpe.br 2 leila.fonseca@inpe.br 3 thales.korting@inpe.br | |||
| Nome do Evento | Workshop dos Cursos de Computação Aplicada do INPE, 17 (WORCAP) | |||
| Localização do Evento | São José dos Campos, SP | |||
| Data | 20-22 nov. 2017 | |||
| Título do Livro | Anais | |||
| Tipo Terciário | Poster | |||
| Histórico (UTC) | 2023-08-29 12:50:09 :: simone -> administrator :: 2023-09-26 02:57:55 :: administrator -> simone :: 2017 | |||
| 3. Conteúdo e estrutura | ||||
| É a matriz ou uma cópia? | é a matriz | |||
| Estágio do Conteúdo | concluido | |||
| Transferível | 1 | |||
| Tipo do Conteúdo | External Contribution | |||
| Tipo de Versão | publisher | |||
| Palavras-Chave | Deep Learning Remote Sensing Machine Learning Image classification | |||
| Resumo | Remote Sensing (RS) techniques have become increasingly important in data-collection tasks and location-based services. Recent increased accessibility of new generation multispectral sensors has improved the complexity required in the analysis techniques. Produce efficient representations and understandings of the scenes has become a challenging problem. To improve knowledge representation and feature description, huge number of algorithms have been developed considering not only the local pixel information, but contextual information obtained from homogeneous regions in images (KÖRTING; GARCIA FONSECA; CÂMARA, 2013; WAçLTER, 2004). However, most approaches lack on learning efficient representations of the images, extracting only shallow features that cannot easily represent the details of complex real data (LECUN; BENGIO; HINTON, 2015; ZHANG; ZHANG; KUMAR, 2016). Deep Learning (DL) techniques, which can learn representative and discriminative features from data, has become a hotspot in the Machine Learning community. They are composed of multiple levels of feature extraction layers. Each level transforms the representation of the previous level into a higher, slightly more abstract model, mapping different levels of abstractions and combining them to model and explore intrinsic correlations of the data (Lecun et al., 2015). DL algorithms have recently started to be used by the RS community, being successfully used in several tasks, from pre-processing to classification. Despite the great potential of these techniques, many questions are still unknown for its use in RS applications. The large number of bands and the way to consider the spectral curves represent a great challenge. Only few labeled samples are available, leading to difficulties to train the network. Images acquired from different sensors or in different seasons have large differences among them, leading to problems to transfer the network knowledge between different images (ZHANG; ZHANG; KUMAR, 2016). The main goal of this work is to investigate the use of Deep Learning based approaches for classification of remote sensing images. We believe that designing an architecture to a Deep Neural Network considering the particularities and complexities of RS images, we can achieve good results for classification. With this approach, we expect to answer some opened questions about the use of DL in RS image analysis, filling in some gaps in the image analysis. Therefore, the main question we aim to answer is What is the best architecture to a Deep Neural Network to classify high resolution remote sensing Images?. A case study was developed in the classification of Land Cover in Brazilian Amazon, with main focus on the deforestation. To train the network and evaluate the results, PRODES deforestation data was used. It is important to emphasize that although this study is in a preliminary stage, the results are promising and reached improvements in the accuracy of the classification. | |||
| Área | COMP | |||
| Arranjo 1 | urlib.net > DIDPI > Deep Learning Techniques... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > SER > Deep Learning Techniques... | |||
| Arranjo 3 | urlib.net > BDMCI > Fonds > WORCAP > XVII WORCAP > Deep Learning Techniques... | |||
| Arranjo 4 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > XVII WORCAP > Deep Learning Techniques... | |||
| 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/8JMKD3MGP3W34P/49NADP5 | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W34P/49NADP5 | |||
| Idioma | en | |||
| Arquivo Alvo | Maretto_deep.pdf | |||
| Grupo de Usuários | simone | |||
| Visibilidade | shown | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3EQCCU5 8JMKD3MGPCW/3F3NU5S 8JMKD3MGPDW34P/49QQESB | |||
| Lista de Itens Citando | sid.inpe.br/mtc-m16c/2023/09.14.00.51 6 sid.inpe.br/bibdigital/2013/09.09.15.05 1 | |||
| Acervo Hospedeiro | sid.inpe.br/mtc-m21b/2013/09.26.14.25.20 | |||
| 6. Notas | ||||
| Campos Vazios | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark type url volume | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||