|
|
Registros recuperados : 5 | |
Registros recuperados : 5 | |
|
|
Registro Completo
Biblioteca(s): |
Embrapa Agricultura Digital. |
Data corrente: |
24/08/2022 |
Data da última atualização: |
25/08/2022 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
B - 3 |
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. |
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. |
Título: |
Multitemporal segmentation of Sentinel-2 images in an agricultural intensification region in Brazil. |
Ano de publicação: |
2022 |
Fonte/Imprenta: |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, v. V-3-2022, p. 389-395, 2022. |
DOI: |
https://doi.org/10.5194/isprs-annals-V-3-2022-389-2022 |
Idioma: |
Inglês |
Notas: |
Edition of proceedings of the 2022 edition of the XXIVth ISPRS Congress, Nice, France. |
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 |
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. |
Thesaurus NAL: |
Vegetation index. |
Categoria do assunto: |
-- |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1145716/1/AP-Multitemporal-segmentation-Sentinel2-2022.pdf
|
Marc: |
LEADER 03126naa a2200397 a 4500 001 2145716 005 2022-08-25 008 2022 bl uuuu u00u1 u #d 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.
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. |
|
|