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Registro Completo |
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Biblioteca(s): |
Embrapa Acre; Embrapa Agrobiologia; Embrapa Agroindústria de Alimentos; Embrapa Agroindústria Tropical; Embrapa Agropecuária Oeste; Embrapa Amapá; Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Clima Temperado; Embrapa Gado de Leite; Embrapa Instrumentação; Embrapa Meio Norte / UEP-Parnaíba; Embrapa Meio-Norte; Embrapa Milho e Sorgo; Embrapa Rondônia; Embrapa Roraima; Embrapa Semiárido; Embrapa Solos; Embrapa Unidades Centrais; Embrapa Uva e Vinho. MenosEmbrapa Acre; Embrapa Agrobiologia; Embrapa Agroindústria de Alimentos; Embrapa Agroindústria Tropical; Embrapa Agropecuária Oeste; Embrapa Amapá; Embrapa Amazônia Ocidental; Embrapa Amazônia Oriental; Embrapa Arroz e Feijão; Embrapa Clima Temperado... Mostrar Todas |
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Data corrente: |
14/09/2017 |
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Data da última atualização: |
04/12/2017 |
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Tipo da produção científica: |
Autoria/Organização/Edição de Livros |
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Autoria: |
CARDOSO, M. J.; BASTOS, E. A.; ANDRADE JUNIOR, A. S. de; ATHAYDE SOBRINHO, C. (ed.). |
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Afiliação: |
MILTON JOSE CARDOSO, CPAMN; EDSON ALVES BASTOS, CPAMN; ADERSON SOARES DE ANDRADE JUNIOR, CPAMN; CANDIDO ATHAYDE SOBRINHO, CPAMN. |
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Título: |
Feijão-Caupi: o produtor pergunta, a Embrapa responde. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
Brasília, DF: Embrapa, 2017. |
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Páginas: |
244 p. |
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Descrição Física: |
il |
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Série: |
(Coleção 500 perguntas, 500 respostas). |
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ISBN: |
978-85-7035-693-2 |
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Idioma: |
Português |
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Conteúdo: |
Este livro contém informações mais recentes sobre a cultura do feijão-caupi, mas dá ênfase ao sistema de produção. Nele são abordados vários temas, entre os quais se destacam: a semeadura de grãos na safra normal e questões atinentes ao feijão-caupi safrinha, cultivos consorciados do feijão-caupi utilizado na alimentação animal, a produção de sementes, a pós-colheita, a secagem e o armazenamento. |
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Palavras-Chave: |
500 perguntas; 500 respostas; Caupi safrinha; Coleção; Consorciação; Ecofisiologia; Feijão-caupi; Manejo cultural; Melhoramento genético; Zoneamento de risco climático. |
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Thesagro: |
Adubação; Armazenamento; Doença fungica; Feijão; Feijão de corda; Industrialização; Melhoramento; Nematoide; Pos-colheita; Praga; Secagem. |
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Categoria do assunto: |
--
E Economia e Indústria Agrícola
F Plantas e Produtos de Origem Vegetal
Q Alimentos e Nutrição Humana |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/166086/1/-files-500p500r-feijao-caupi.epub
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/166168/1/500P500R-Feijao-caupi.pdf
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Marc: |
LEADER 01569nam a2200433 a 4500
001 2075578
005 2017-12-04
008 2017 bl uuuu 00u1 u #d
020 $a978-85-7035-693-2
100 1 $aCARDOSO, M. J.
245 $aFeijão-Caupi$bo produtor pergunta, a Embrapa responde.
260 $aBrasília, DF: Embrapa$c2017
300 $a244 p.$cil
490 $a(Coleção 500 perguntas, 500 respostas).
520 $aEste livro contém informações mais recentes sobre a cultura do feijão-caupi, mas dá ênfase ao sistema de produção. Nele são abordados vários temas, entre os quais se destacam: a semeadura de grãos na safra normal e questões atinentes ao feijão-caupi safrinha, cultivos consorciados do feijão-caupi utilizado na alimentação animal, a produção de sementes, a pós-colheita, a secagem e o armazenamento.
650 $aAdubação
650 $aArmazenamento
650 $aDoença fungica
650 $aFeijão
650 $aFeijão de corda
650 $aIndustrialização
650 $aMelhoramento
650 $aNematoide
650 $aPos-colheita
650 $aPraga
650 $aSecagem
653 $a500 perguntas
653 $a500 respostas
653 $aCaupi safrinha
653 $aColeção
653 $aConsorciação
653 $aEcofisiologia
653 $aFeijão-caupi
653 $aManejo cultural
653 $aMelhoramento genético
653 $aZoneamento de risco climático
700 1 $aBASTOS, E. A.
700 1 $aANDRADE JUNIOR, A. S. de
700 1 $aATHAYDE SOBRINHO, C.
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Registro original: |
Embrapa Unidades Centrais (AI-SEDE) |
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Biblioteca |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Territorial. Para informações adicionais entre em contato com cnpm.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
14/08/2020 |
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Data da última atualização: |
17/08/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
MARTINS, V. S.; KALEITA, A. L.; GELDER, B. K.; SILVEIRA, H. L. F. da; ABE, C. A. |
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Afiliação: |
VITOR S. MARTINS, IOWA STATE UNIVERSITY; AMY L. KALEITA, IOWA STATE UNIVERSITY; BRIAN K. GELDER, IOWA STATE UNIVERSITY; HILTON LUIS FERRAZ DA SILVEIRA, CNPM; CAMILA A. ABE, UNIVERSITY OF WISCONSIN-MADISON. |
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Título: |
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. |
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Ano de publicação: |
2020 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 56-73, oct. 2020. |
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ISBN: |
0924-2716 |
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DOI: |
https://doi.org/10.1016/j.isprsjprs.2020.08.004 |
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Idioma: |
Inglês |
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Conteúdo: |
Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (?medial axis?) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution. MenosConvolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (?medial axis?) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented... Mostrar Tudo |
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Palavras-Chave: |
Aerial imagery; Convolutional neural network; Deep learning. |
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Thesaurus NAL: |
Land cover. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 03374naa a2200241 a 4500
001 2124365
005 2020-08-17
008 2020 bl uuuu u00u1 u #d
022 $a0924-2716
024 7 $ahttps://doi.org/10.1016/j.isprsjprs.2020.08.004$2DOI
100 1 $aMARTINS, V. S.
245 $aExploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution.$h[electronic resource]
260 $c2020
520 $aConvolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces coarse maps using a sliding window approach. To address this problem, object-based CNN (OCNN) becomes an alternative solution to improve classification performance. However, previous studies were mainly focused on urban areas or small scenes, and implementation of OCNN method is still needed for large-area classification over heterogeneous landscape. Additionally, the massive labeling of segmented objects requires a practical approach for less computation, including object analysis and multiple CNNs. This study presents a new multiscale OCNN (multi-OCNN) framework for large-scale land cover classification at 1-m resolution over 145,740 km2. Our approach consists of three main steps: (i) image segmentation, (ii) object analysis with skeleton-based algorithm, and (iii) application of multiple CNNs for final classification. Also, we developed a large benchmark dataset, called IowaNet, with 1 million labeled images and 10 classes. In our approach, multiscale CNNs were trained to capture the best contextual information during the semantic labeling of objects. Meanwhile, skeletonization algorithm provided morphological representation (?medial axis?) of objects to support the selection of convolutional locations for CNN predictions. In general, proposed multi-OCNN presented better classification accuracy (overall accuracy ~87.2%) compared to traditional patch-based CNN (81.6%) and fixed-input OCNN (82%). In addition, the results showed that this framework is 8.1 and 111.5 times faster than traditional pixel-wise CNN16 or CNN256, respectively. Multiple CNNs and object analysis have proved to be essential for accurate and fast classification. While multi-OCNN produced a high-level of spatial details in the land cover product, misclassification was observed for some classes, such as road versus buildings or shadow versus lake. Despite these minor drawbacks, our results also demonstrated the benefits of IowaNet training dataset in the model performance; overfitting process reduces as the number of samples increases. The limitations of multi-OCNN are partially explained by segmentation quality and limited number of spectral bands in the aerial data. With the advance of deep learning methods, this study supports the claim of multi-OCNN benefits for operational large-scale land cover product at 1-m resolution.
650 $aLand cover
653 $aAerial imagery
653 $aConvolutional neural network
653 $aDeep learning
700 1 $aKALEITA, A. L.
700 1 $aGELDER, B. K.
700 1 $aSILVEIRA, H. L. F. da
700 1 $aABE, C. A.
773 $tISPRS Journal of Photogrammetry and Remote Sensing$gv. 168, p. 56-73, oct. 2020.
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