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| Registros recuperados : 39 | |
| 21. |  | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; LAMPARELLI, R. A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification in a heterogeneous agricultural landscape using PlanetScope data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. XLVIII-M-1-2023, p. 49-55, 2023. Edition of proceedings of the 39th International Symposium on Remote Sensing of Environment (ISRSE-39) "From Human Needs to SDGs", 2023, Antalya, Türkiye.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 22. | | BUENO, I. T.; ANTUNES, J. F. G.; TORO, A. P. S. G. D.; WERNER, J. P. S.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G. Land use/land cover classification and scale effect analysis for a multi-temporal superpixel-based segmentation using PlanetScope data. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 304-307. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 23. | | SILVA, Y. F.; VALADARES, R. V.; DIAS, H. B.; CUADRA, S. V.; CAMPBELL, E. E.; LAMPARELLI, R. A. C.; MORO, E.; BATTISTI, R.; ALVES, M. R.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. Intense pasture management in Brazil in an integrated crop-livestock system simulated by the DayCent model. Sustainability, v. 14, n. 6, p. 1-24, Mar. 2022. Article 3517.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 24. | | GREGO, C. R.; ARAUJO, L. S. de; VICENTE, L. E.; NOGUEIRA, S. F.; MAGALHÃES, P. S. G.; VICENTE, A. K.; BRANCALIÃO, S. R.; VICTORIA, D. de C.; BOLFE, E. L. Agricultura de precisão em cana-de-açúcar. In: BERNARDI, A. C. de C.; NAIME, J. de M.; RESENDE, A. V. de; BASSOI, L. H.; INAMASU, R. Y. (Ed.). Agricultura de precisão: resultados de um novo olhar. Brasília, DF: Embrapa, 2014. p. 442-457.| Biblioteca(s): Embrapa Territorial. |
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| 25. | | ALMEIDA, H. S. L.; REIS, A. A. dos; WERNER, J. P. S.; ANTUNES, J. F. G.; ZHONG, L.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Deep neural networks for mapping integrated crop-livestock systems using PlanetScope time series. IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2021, Brussels. Proceedings [...]. [S. l.]: IEEE, 2021. p. 4224-4227. IGARSS 2021. Paper WE2.MM-8.3.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 26. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C. da; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G. Can canopy height of mixed pastures in integrated crop-livestock systems be estimated using planetscope imagery? In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference. Brasília, DF: Embrapa, 2021. p. 658-663. WCCLF 2021. Evento online.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 27. | | TORO, A. P. S. G. D. D.; BUENO, I. T.; WERNER, J. P. S.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. SAR and optical data applied to early-season mapping of integrated crop-livestock systems using deep and machine learning algorithms. Remote Sensing, v. 15, n. 4, 1130, Feb. 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 28. | | BUENO, I. T.; ANTUNES, J. F. G.; REIS, A. A. dos; WERNER, J. P. S.; TORO, A. P.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; MAGALHÃES, P. S. G. Mapping integrated crop-livestock systems in Brazil with planetscope time series and deep learning. Remote Sensing of Environment, v. 299, 113886, Dec. 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 29. | | DIAS, H. B.; CUADRA, S. V.; FIGUEIREDO, G. K. D. A.; LAMPARELLI, R. A. C.; SILVA, L. E. A.; SILVA, Y. F. da; MORO, E.; ALVES, M. R.; MAGALHÃES, P. S. G. Modelling integrated crop-livestock systems: preliminary results from an agroecosystem model. In: WORLD CONGRESS ON INTEGRATED CROP-LIVESTOCK-FORESTRY SYSTEMS, 2., 2021. Proceedings reference. Brasília, DF: Embrapa, 2021. p. 782-787. WCCLF 2021. Evento online.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 30. | | REIS, A. A. dos; WERNER, J. P. S.; SILVA, B. C.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; ROCHA, J. V.; MAGALHÃES, P. S. G. Monitoring pasture aboveground biomass and canopy height in an integrated crop-livestock system using textural information from PlanetScope imagery. Remote Sensing, v. 12, n. 16, p. 1-21, Aug. 2020. Article number: 2534.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 31. | | PEREIRA, F. R. da S.; REIS, A. A. dos; FREITAS, R. G.; OLIVEIRA, S. R. de M.; AMARAL, L. R. do; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; MORO, E.; MAGALHÃES, P. S. G. Imputation of missing parts in UAV orthomosaics using PlanetScope and Sentinel-2 data: a case study in a grass-dominated área. International Journal of Geo-Information, v. 12, n. 2, 41, Feb. 2023.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 32. | | TORO, A. P. S. G. D.; WERNER, J. P. S.; REIS, A. A. dos; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; FIGUEIREDO, G. K. D. A. Evaluation of early season mapping of integrated crop livestock systems using Sentinel-2 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 43, B3, p. 1335-1340, 2022. Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 33. | | REIS, A. A. dos; SILVA, B. C.; WERNER, J. P. S.; SILVA, Y. F.; ROCHA, J. V.; FIGUEIREDO, G. K. D. A.; ANTUNES, J. F. G.; ESQUERDO, J. C. D. M.; COUTINHO, A. C.; LAMPARELLI, R. A. C; MAGALHÃES, P. S. G. Exploring the potential of high-resolution PlanetScope imagery for pasture biomass estimation in an integrated crop-livestock system. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. 42-3, W12, p. 419-424, 2020. Publicado também em: IEEE LATIN AMERICAN GRSS; ISPRS REMOTE SENSING CONFERENCE, Santiago, 2020. Proceedings... [Piscataway]: IEEE, 2020. p. 675-680. LAGIRS 2020.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 34. | | WERNER, J. P. S.; REIS, A. A. dos; TORO, A. P. S. G. D.; BUENO, I. T.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A. Temporal comparison of multiple sensors for monitoring paddock management in an integrated crop-livestock system. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 869-872. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 35. | | ANTUNES, J. F. G.; REIS, A. A. dos; ALMEIDA, H. S. L.; WERNER, J. P. S.; FIGUEIREDO, G. K. D. A.; ESQUERDO, J. C. D. M.; BUENO, I. T.; TORO, A. P. S. G. D.; LAMPARELLI, R. A. C.; COUTINHO, A. C.; MAGALHÃES, P. S. G. Classification of integrated crop-livestock systems using PlanetScope time series. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 916-919. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 36. | | TORO, A. P. S. G. D.; CAPUCCI, G.; WERNER, J. P. S.; BUENO, I. T.; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; OLIVEIRA JÚNIOR, J. G.; FIGUEIREDO, G. K. D. A. Effects of the different precipitation levels on SAR data. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 3088-3091. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 37. | | WERNER, J. P. S.; BELGIU, M.; BUENO, I. T.; REIS, A. A. dos; TORO, A. P. S. G. D.; ANTUNES, J. F. G.; STEIN, A.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; COUTINHO, A. C.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A. Mapping integrated crop–livestock systems using fused Sentinel-2 and PlanetScope time series and deep learning. Remote Sensing, v. 16, n. 8, p. 1421, Apr. 2024.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 38. | | SANTOS, L. T. dos; WERNER, J. P. S.; REIS, A. A. dos; TORO, A. P. G.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A. Multitemporal segmentation of Sentinel-2 images in an agricultural intensification region in Brazil. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. V-3-2022, p. 389-395, 2022. Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.| Biblioteca(s): Embrapa Agricultura Digital. |
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| 39. | | OLIVEIRA JÚNIOR, J. G. de; WERNER, J. P. S.; TORO, A. P. S. G. D.; BUENO, I. T.; ESQUERDO, J. C. D. M.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; SHIBUYA, D. H.; JOAQUIM, L. F.; FIGUEIREDO, G. K. D. A. Verificação do potencial de sistemas de integração lavoura-pecuária na recuperação de áreas de pastagens degradadas. In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 20., 2023, Florianópolis. Anais [...]. São José dos Campos: Instituto Nacional de Pesquisas Espaciais, 2023. p. 1858-1861. Editores: Douglas Francisco Marcolino Gherardi, Ieda Del´Arco Sanches, Luiz Eduardo Oliveira e Cruz de Aragão.| Biblioteca(s): Embrapa Agricultura Digital. |
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| Registros recuperados : 39 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
24/08/2022 |
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Data da última atualização: |
25/08/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 3 |
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Autoria: |
SANTOS, L. T. dos; WERNER, J. P. S.; REIS, A. A. dos; TORO, A. P. G.; ANTUNES, J. F. G.; COUTINHO, A. C.; LAMPARELLI, R. A. C.; MAGALHÃES, P. S. G.; ESQUERDO, J. C. D. M.; FIGUEIREDO, G. K. D. A. |
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Afiliação: |
FEAGRI/UNICAMP; FEAGRI/UNICAMP; UNICAMP; FEAGRI/UNICAMP; JOAO FRANCISCO GONCALVES ANTUNES, CNPTIA; ALEXANDRE CAMARGO COUTINHO, CNPTIA; UNICAMP; UNICAMP; JULIO CESAR DALLA MORA ESQUERDO, CNPTIA, FEAGRI/UNICAMP; FEAGRI/UNICAMP. |
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Título: |
Multitemporal segmentation of Sentinel-2 images in an agricultural intensification region in Brazil. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. V-3-2022, p. 389-395, 2022. |
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DOI: |
https://doi.org/10.5194/isprs-annals-V-3-2022-389-2022 |
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Idioma: |
Inglês |
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Notas: |
Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. |
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Conteúdo: |
ABSTRACT: With the recent evolution in the sensor's spatial resolution, such as the MultiSpectral Imager (MSI) of the Sentinel-2 mission, the need to use segmentation techniques in satellite images has increased. Although the advantages of image segmentation to delineate agricultural fields in images are already known, the literature shows that it is rarely used to consider temporal changes in highly managed regions with the intensification of agricultural activities. Therefore, this work aimed to evaluate a multitemporal segmentation method based on the coefficient of variation of spectral bands and vegetation indices obtained from Sentinel-2 images, considering two agricultural years (2018-2019 and 2019-2020) in an area with agricultural intensification. Images of the coefficient of variation represented the spectro-temporal dynamics within the study area. These images were also used to apply an edge detection filter (Sobel) to verify their performance. The region-based algorithm Watershed Segmentation (WS) was used in the segmentation process. Subsequently, to assess the quality of the segmentation results produced, the metrics Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2) were calculated from manually delineated reference objects. The segmentation achieved its best performance when applied to the unfiltered coefficient of variation images of spectral bands with an ED2 equal to 7.289 and 2.529 for 2018-2019 and 2019-2020, respectively. There was a tendency for the WS algorithm to produce over-segmentation in the study area; however, its use proved to be effective in identifying objects in a dynamic area with the intensification of agricultural activities. MenosABSTRACT: With the recent evolution in the sensor's spatial resolution, such as the MultiSpectral Imager (MSI) of the Sentinel-2 mission, the need to use segmentation techniques in satellite images has increased. Although the advantages of image segmentation to delineate agricultural fields in images are already known, the literature shows that it is rarely used to consider temporal changes in highly managed regions with the intensification of agricultural activities. Therefore, this work aimed to evaluate a multitemporal segmentation method based on the coefficient of variation of spectral bands and vegetation indices obtained from Sentinel-2 images, considering two agricultural years (2018-2019 and 2019-2020) in an area with agricultural intensification. Images of the coefficient of variation represented the spectro-temporal dynamics within the study area. These images were also used to apply an edge detection filter (Sobel) to verify their performance. The region-based algorithm Watershed Segmentation (WS) was used in the segmentation process. Subsequently, to assess the quality of the segmentation results produced, the metrics Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2) were calculated from manually delineated reference objects. The segmentation achieved its best performance when applied to the unfiltered coefficient of variation images of spectral bands with an ED2 equal to 7.289 and 2.529 for 2018-2019 and 2019-202... Mostrar Tudo |
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Palavras-Chave: |
AssesSeg; Coefficient of variation; Coeficiente de variação; Detecção de bordas; Edge detection; Índice de vegetação; Intensificação agrícola; OBIA; Segmentação de bacias hidrográficas; Sobel; Watershed segmentation. |
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Thesaurus NAL: |
Vegetation index. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1145716/1/AP-Multitemporal-segmentation-Sentinel2-2022.pdf
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Marc: |
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024 7 $ahttps://doi.org/10.5194/isprs-annals-V-3-2022-389-2022$2DOI
100 1 $aSANTOS, L. T. dos
245 $aMultitemporal segmentation of Sentinel-2 images in an agricultural intensification region in Brazil.$h[electronic resource]
260 $c2022
500 $aEdition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France.
520 $aABSTRACT: With the recent evolution in the sensor's spatial resolution, such as the MultiSpectral Imager (MSI) of the Sentinel-2 mission, the need to use segmentation techniques in satellite images has increased. Although the advantages of image segmentation to delineate agricultural fields in images are already known, the literature shows that it is rarely used to consider temporal changes in highly managed regions with the intensification of agricultural activities. Therefore, this work aimed to evaluate a multitemporal segmentation method based on the coefficient of variation of spectral bands and vegetation indices obtained from Sentinel-2 images, considering two agricultural years (2018-2019 and 2019-2020) in an area with agricultural intensification. Images of the coefficient of variation represented the spectro-temporal dynamics within the study area. These images were also used to apply an edge detection filter (Sobel) to verify their performance. The region-based algorithm Watershed Segmentation (WS) was used in the segmentation process. Subsequently, to assess the quality of the segmentation results produced, the metrics Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2) were calculated from manually delineated reference objects. The segmentation achieved its best performance when applied to the unfiltered coefficient of variation images of spectral bands with an ED2 equal to 7.289 and 2.529 for 2018-2019 and 2019-2020, respectively. There was a tendency for the WS algorithm to produce over-segmentation in the study area; however, its use proved to be effective in identifying objects in a dynamic area with the intensification of agricultural activities.
650 $aVegetation index
653 $aAssesSeg
653 $aCoefficient of variation
653 $aCoeficiente de variação
653 $aDetecção de bordas
653 $aEdge detection
653 $aÍndice de vegetação
653 $aIntensificação agrícola
653 $aOBIA
653 $aSegmentação de bacias hidrográficas
653 $aSobel
653 $aWatershed segmentation
700 1 $aWERNER, J. P. S.
700 1 $aREIS, A. A. dos
700 1 $aTORO, A. P. G.
700 1 $aANTUNES, J. F. G.
700 1 $aCOUTINHO, A. C.
700 1 $aLAMPARELLI, R. A. C.
700 1 $aMAGALHÃES, P. S. G.
700 1 $aESQUERDO, J. C. D. M.
700 1 $aFIGUEIREDO, G. K. D. A.
773 $tISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences$gv. V-3-2022, p. 389-395, 2022.
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