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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
06/11/2013 |
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Data da última atualização: |
06/11/2013 |
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Autoria: |
MACHADO, C. A. |
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Título: |
Exploração florestal. |
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Ano de publicação: |
1984 |
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Fonte/Imprenta: |
Viçosa, MG: Imprensa da Universidade Federal de Viçosa, 1984. |
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Páginas: |
34 p. |
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Idioma: |
Português |
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Notas: |
III parte. |
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Thesagro: |
Exploração Florestal. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00360nam a2200133 a 4500
001 1970502
005 2013-11-06
008 1984 bl uuuu 00u1 u #d
100 1 $aMACHADO, C. A.
245 $aExploração florestal.
260 $aViçosa, MG: Imprensa da Universidade Federal de Viçosa$c1984
300 $a34 p.
500 $aIII parte.
650 $aExploração Florestal
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Registro original: |
Embrapa Florestas (CNPF) |
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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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Embrapa Territorial (CNPM) |
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