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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Ambiente. |
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Data corrente: |
25/01/2016 |
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Data da última atualização: |
04/01/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
SCHULTZ, B.; IMMITZER, M.; FORMAGGIO, A. R.; SANCHES, I. D. A.; LUIZ, A. J. B.; ATZBERGER, C. |
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Afiliação: |
BRUNO SCHULTZ, INPE; MARCUS IMMITZER, University of Natural Resources and Life Sciences, Viena; ANTONIO ROBERTO FORMAGGIO, INPE; IEDA DEL'ARCO SANCHES, INPE; ALFREDO JOSE BARRETO LUIZ, CNPMA; CLEMENT ATZBERGER, University of Natural Resources and Life Sciences, Viena. |
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Título: |
Self-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Remote Sensing, Basel, v. 7, n. 11, p. 14482-14508, 2015. |
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ISBN: |
http://dx.doi.org/10.3390/rs71114482 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map. MenosAbstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanu... Mostrar Tudo |
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Palavras-Chave: |
Crop mapping; Mapeamento agrícola; Multi-resolution segmentation; OBIA; OLI; Random forest; Segmentação multirresolução. |
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Thesagro: |
Sensoriamento remoto. |
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Thesaurus Nal: |
Brazil; Remote sensing. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/137582/1/2015AP38.pdf
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Marc: |
LEADER 02967naa a2200301 a 4500
001 2034915
005 2023-01-04
008 2015 bl uuuu u00u1 u #d
100 1 $aSCHULTZ, B.
245 $aSelf-guided segmentation and classification of multi-temporal landsat 8 images for crop type mapping in southeastern Brazil.$h[electronic resource]
260 $c2015
520 $aAbstract: Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ? 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.
650 $aBrazil
650 $aRemote sensing
650 $aSensoriamento remoto
653 $aCrop mapping
653 $aMapeamento agrícola
653 $aMulti-resolution segmentation
653 $aOBIA
653 $aOLI
653 $aRandom forest
653 $aSegmentação multirresolução
700 1 $aIMMITZER, M.
700 1 $aFORMAGGIO, A. R.
700 1 $aSANCHES, I. D. A.
700 1 $aLUIZ, A. J. B.
700 1 $aATZBERGER, C.
773 $tRemote Sensing, Basel$gv. 7, n. 11, p. 14482-14508, 2015.
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Registro original: |
Embrapa Meio Ambiente (CNPMA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
28/07/2011 |
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Data da última atualização: |
28/07/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
ARAÚJO, L. C. de; SANTOS, P. M.; MENDONCA, F. C.; MOURÃO, G. B. |
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Afiliação: |
LEANDRO COELHO DE ARAÚJO, ESALQ-USP/PIRACICABA; PATRICIA MENEZES SANTOS, CPPSE; FERNANDO CAMPOS MENDONCA, CPPSE; GERSON BARRETO MOURÃO, ESALQ-USP/PIRACICABA. |
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Título: |
Establishment of Brachiaria brizantha cv. Marandu, under levels of soil water availability in stages of growth of the plants. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Revista Brasileira de Zootecnia, v. 40, n. 7, p. 1405-1411, jul. 2011. |
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DOI: |
https://doi.org/10.1590/S1516-35982011000700002 |
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Idioma: |
Inglês |
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Conteúdo: |
The objective of this work was to evaluate yield traits and development of palisadegrass under the influence of water deficit during the establishment period. The experiment was carried out in a greenhouse, in a completely random block statistical design in a factorial arrangement and additional treatment (3 × 3 + 1). The treatments referred to the suppression of irrigation at different phases of the establishment (sowing, germination and initial tillerring) until the soil presented water content of 75%, 50%, and 25% of the moisture related to field capacity (qFC), besides control treatment with no water restriction. Evaluations of number of grown tillers per vase, green leaves per tiller and plant height were carried out weekly, for five weeks after the first tillers appeared. Biomass sampling was carried out approximately 30 days after the end of the last applied treatment, when the soil was kept close to 100% of field capacity relative moisture. Tillering and biomass yield of palisadegrass during establishment phase are reduced when water deficit is sufficient to make soil content water reach 25% of relative moisture field capacity, regardless to the season when water shortage takes place. |
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Palavras-Chave: |
Drough; Palisadegrass. |
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Thesaurus NAL: |
Biomass; Soil water deficit; Tillering. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/38805/1/PROCI-2011.00073.pdf
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Marc: |
LEADER 01958naa a2200229 a 4500
001 1896921
005 2022-07-28
008 2011 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1590/S1516-35982011000700002$2DOI
100 1 $aARAÚJO, L. C. de
245 $aEstablishment of Brachiaria brizantha cv. Marandu, under levels of soil water availability in stages of growth of the plants.$h[electronic resource]
260 $c2011
520 $aThe objective of this work was to evaluate yield traits and development of palisadegrass under the influence of water deficit during the establishment period. The experiment was carried out in a greenhouse, in a completely random block statistical design in a factorial arrangement and additional treatment (3 × 3 + 1). The treatments referred to the suppression of irrigation at different phases of the establishment (sowing, germination and initial tillerring) until the soil presented water content of 75%, 50%, and 25% of the moisture related to field capacity (qFC), besides control treatment with no water restriction. Evaluations of number of grown tillers per vase, green leaves per tiller and plant height were carried out weekly, for five weeks after the first tillers appeared. Biomass sampling was carried out approximately 30 days after the end of the last applied treatment, when the soil was kept close to 100% of field capacity relative moisture. Tillering and biomass yield of palisadegrass during establishment phase are reduced when water deficit is sufficient to make soil content water reach 25% of relative moisture field capacity, regardless to the season when water shortage takes place.
650 $aBiomass
650 $aSoil water deficit
650 $aTillering
653 $aDrough
653 $aPalisadegrass
700 1 $aSANTOS, P. M.
700 1 $aMENDONCA, F. C.
700 1 $aMOURÃO, G. B.
773 $tRevista Brasileira de Zootecnia$gv. 40, n. 7, p. 1405-1411, jul. 2011.
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Embrapa Pecuária Sudeste (CPPSE) |
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