| |
|
|
 | Acesso ao texto completo restrito à biblioteca da Embrapa Territorial. Para informações adicionais entre em contato com cnpm.biblioteca@embrapa.br. |
|
Registro Completo |
|
Biblioteca(s): |
Embrapa Territorial. |
|
Data corrente: |
14/08/2020 |
|
Data da última atualização: |
17/08/2020 |
|
Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Autoria: |
MARTINS, V. S.; KALEITA, A. L.; GELDER, B. K.; SILVEIRA, H. L. F. da; ABE, C. A. |
|
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. |
|
Título: |
Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. |
|
Ano de publicação: |
2020 |
|
Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 168, p. 56-73, oct. 2020. |
|
ISBN: |
0924-2716 |
|
DOI: |
https://doi.org/10.1016/j.isprsjprs.2020.08.004 |
|
Idioma: |
Inglês |
|
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 |
|
Palavras-Chave: |
Aerial imagery; Convolutional neural network; Deep learning. |
|
Thesaurus Nal: |
Land cover. |
|
Categoria do assunto: |
-- |
|
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.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Territorial (CNPM) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
URL |
Voltar
|
|
|
| Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
Registro Completo
|
Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Pecuária Sudeste. |
|
Data corrente: |
09/10/2023 |
|
Data da última atualização: |
24/10/2023 |
|
Tipo da produção científica: |
Artigo em Anais de Congresso |
|
Autoria: |
AFONSO, J.; FORTES, M. R. S.; SHIM, W. J.; CARDOSO, T. F.; BRUSCADIN, J. J.; LIMA, A. O. de; DINIZ, W. J. S.; SILVA-VIGNATO, B.; TAN, W. L. A.; CESAR, A. S. M.; BODEN, M.; MOURÃO, G. B.; ZERLOTINI NETO, A.; COUTINHO, L. L.; REGITANO, L. C. de A. |
|
Afiliação: |
MARINA RUFINO SALINAS FORTES, THE UNIVERSITY OF QUEENSLAND; JENNIFER JESSICA BRUSCADIN, UNIVERSIDADE FEDERAL DE SÃO CARLOS; ANDRESSA OLIVEIRA DE LIMA, UNIVERSITY OF WASHINGTON; AUBURN UNIVERSITY; UNIVERSIDADE DE SÃO PAULO; THE UNIVERSITY OF QUEENSLAND; ALINE SILVA MELLO CESAR, UNIVERSIDADE DE SÃO PAULO; MIKAEL BODEN, THE UNIVERSITY OF QUEENSLAND; GERSON BARRETO MOURÃO, UNIVERSIDADE DE SÃO PAULO; ADHEMAR ZERLOTINI NETO, CNPTIA; UNIVERSIDADE DE SÃO PAULO; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE. |
|
Título: |
Epigenetic repression of genes associated with ribeye area of nelore cattle. |
|
Ano de publicação: |
2023 |
|
Fonte/Imprenta: |
In: CONFERENCE OF THE ASSOCIATION FOR THE ADVANCEMENT OF ANIMAL BREEDING AND GENETICS, 25., 2023, Perth. Animal breeding at the crossroads: proceedings. Armidale: Association for the Advancement of Animal Breeding and Genetics, 2023. p. 206-209. |
|
ISSN: |
1328-3227 |
|
Idioma: |
Inglês |
|
Notas: |
AAABG 2023. Na publicação: A. Zerlotini. |
|
Conteúdo: |
SUMMARY. Understanding the epigenetic repression role in regulating genes involved with the ribeye area (REA) of bovine muscle can help us to predict this trait in the future. Here, we identified genes putatively regulating REA in Nelore cattle and divergently epigenetically repressed between contrasting sample groups. For that, we applied the TRIAGE method with a Rank Product analysis using bovine muscle expression data on high versus low REA groups. Further, we identified overrepresented pathways and biological processes linked to candidate genes, searching for their regulatory direction. This result advances the knowledge about how epigenetic regulation may impact production traits in Nelore cattle. |
|
Palavras-Chave: |
Área de olho de lombo; Candidate regulatory genes; Epigenetic repression; Genes candidatos a reguladores; Longissimus thoracis muscle´s expression data; Repressão epigenética; Ribeye area. |
|
Thesagro: |
Gado Nelore. |
|
Categoria do assunto: |
--
G Melhoramento Genético |
|
Marc: |
LEADER 02118nam a2200397 a 4500
001 2157146
005 2023-10-24
008 2023 bl uuuu u00u1 u #d
022 $a1328-3227
100 1 $aAFONSO, J.
245 $aEpigenetic repression of genes associated with ribeye area of nelore cattle.$h[electronic resource]
260 $aIn: CONFERENCE OF THE ASSOCIATION FOR THE ADVANCEMENT OF ANIMAL BREEDING AND GENETICS, 25., 2023, Perth. Animal breeding at the crossroads: proceedings. Armidale: Association for the Advancement of Animal Breeding and Genetics, 2023. p. 206-209.$c2023
500 $aAAABG 2023. Na publicação: A. Zerlotini.
520 $aSUMMARY. Understanding the epigenetic repression role in regulating genes involved with the ribeye area (REA) of bovine muscle can help us to predict this trait in the future. Here, we identified genes putatively regulating REA in Nelore cattle and divergently epigenetically repressed between contrasting sample groups. For that, we applied the TRIAGE method with a Rank Product analysis using bovine muscle expression data on high versus low REA groups. Further, we identified overrepresented pathways and biological processes linked to candidate genes, searching for their regulatory direction. This result advances the knowledge about how epigenetic regulation may impact production traits in Nelore cattle.
650 $aGado Nelore
653 $aÁrea de olho de lombo
653 $aCandidate regulatory genes
653 $aEpigenetic repression
653 $aGenes candidatos a reguladores
653 $aLongissimus thoracis muscle´s expression data
653 $aRepressão epigenética
653 $aRibeye area
700 1 $aFORTES, M. R. S.
700 1 $aSHIM, W. J.
700 1 $aCARDOSO, T. F.
700 1 $aBRUSCADIN, J. J.
700 1 $aLIMA, A. O. de
700 1 $aDINIZ, W. J. S.
700 1 $aSILVA-VIGNATO, B.
700 1 $aTAN, W. L. A.
700 1 $aCESAR, A. S. M.
700 1 $aBODEN, M.
700 1 $aMOURÃO, G. B.
700 1 $aZERLOTINI NETO, A.
700 1 $aCOUTINHO, L. L.
700 1 $aREGITANO, L. C. de A.
Download
Esconder MarcMostrar Marc Completo |
|
Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
|
|
Biblioteca |
ID |
Origem |
Tipo/Formato |
Classificação |
Cutter |
Registro |
Volume |
Status |
Fechar
|
| Nenhum registro encontrado para a expressão de busca informada. |
|
|
