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Registro Completo |
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Biblioteca(s): |
Embrapa Suínos e Aves. |
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Data corrente: |
11/01/2008 |
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Data da última atualização: |
12/03/2008 |
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Tipo da produção científica: |
Artigo em Anais de Congresso / Nota Técnica |
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Autoria: |
KUNZ, A.; VANOTTI, M.; SZOGI, A.; GARCIA, M. C.; SCHIERHOLT NETO, G. F.; SOARES, H. M. |
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Afiliação: |
Airton Kunz, Embrapa Suínos e Aves; Matias Vanotti, USDA-ARS; Ariel Szogi, USDA - ARS; Maria Cruz Garcia, Instituto de Castilla e Leon; Guilherme Francisco Schierholt Neto, Universidade Federal de Santa Catarina; H. M. Soares. |
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Título: |
Development of Anammox process for animal waste treatment: experiences in Brazil. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
In: INTERNATIONAL SYMPOSIUM ON AIR QUALITY AND WASTE MANAGEMENT TO AGRICULTURE, 2007, Broomfield. Proceedings. Broomfield: [s.n.], 2007. |
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Páginas: |
Não paginado. |
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Idioma: |
Inglês |
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Notas: |
Projeto n. 02.03.21.800-07 |
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Palavras-Chave: |
Dejetos. |
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Thesagro: |
Animal; Tratamento. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00720naa a2200229 a 4500
001 1433702
005 2008-03-12
008 2007 bl --- 0-- u #d
100 1 $aKUNZ, A.
245 $aDevelopment of Anammox process for animal waste treatment$bexperiences in Brazil.
260 $c2007
300 $aNão paginado.
500 $aProjeto n. 02.03.21.800-07
650 $aAnimal
650 $aTratamento
653 $aDejetos
700 1 $aVANOTTI, M.
700 1 $aSZOGI, A.
700 1 $aGARCIA, M. C.
700 1 $aSCHIERHOLT NETO, G. F.
700 1 $aSOARES, H. M.
773 $tIn: INTERNATIONAL SYMPOSIUM ON AIR QUALITY AND WASTE MANAGEMENT TO AGRICULTURE, 2007, Broomfield. Proceedings. Broomfield: [s.n.], 2007.
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Registro original: |
Embrapa Suínos e Aves (CNPSA) |
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Registro Completo
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
17/05/2012 |
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Data da última atualização: |
16/09/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
LI, G.; LU, D.; DUTRA, L.; BATISTELLA, M. |
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Afiliação: |
GUIYING LI, INDIANA UNIVERSITY; DENGSHENG LU, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
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Título: |
A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. |
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Ano de publicação: |
2012 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry and Remote Sensing, v. 70, p. 26-38, 2012. |
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Páginas: |
p. 26-38. |
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Idioma: |
Inglês |
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Conteúdo: |
This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. MenosThis paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover cla... Mostrar Tudo |
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Palavras-Chave: |
ALOS PALSAR; Amazon; Land-cover classification; RADARSAT. |
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Thesaurus NAL: |
texture. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/59517/1/MateusISPRS.pdf
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Marc: |
LEADER 02748naa a2200229 a 4500
001 1924819
005 2014-09-16
008 2012 bl uuuu u00u1 u #d
100 1 $aLI, G.
245 $aA comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region.
260 $c2012
300 $ap. 26-38.
520 $aThis paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms ? maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better landcover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification.
650 $atexture
653 $aALOS PALSAR
653 $aAmazon
653 $aLand-cover classification
653 $aRADARSAT
700 1 $aLU, D.
700 1 $aDUTRA, L.
700 1 $aBATISTELLA, M.
773 $tISPRS Journal of Photogrammetry and Remote Sensing$gv. 70, p. 26-38, 2012.
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