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 | Acesso ao texto completo restrito à biblioteca da Embrapa Solos. Para informações adicionais entre em contato com cnps.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Solos. |
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Data corrente: |
10/01/2007 |
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Data da última atualização: |
04/10/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MOTA, G. L. A.; FEITOSA, R. Q.; COUTINHO, H. L. C.; LIEDTKE, C. E.; MÜLLER, S; PAKZAD, K.; MEIRELLES, M. S. P. |
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Afiliação: |
Guilherme L. A. Mota, PUC-RIO/UERJ; Raul Q. Feitosa, PUC-RIO/UERJ; HEITOR LUIZ DA COSTA COUTINHO, CNPS; Claus-Eberhard Liedtke, University of Hannover; Sönke Müller, University of Hannover; Kian Pakzad, University of Hannover; MARGARETH GONCALVES SIMOES, CNPS. |
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Título: |
Multitemporal fuzzy classification model based on class transition possibilities. |
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Ano de publicação: |
2007 |
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Fonte/Imprenta: |
ISPRS Journal of Photogrammetry & Remote Sensing, Amsterdam, v. 62, n. 3, p. 186-200, Aug. 2007. |
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DOI: |
10.1016/j.isprsjprs.2007.04.001 |
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Idioma: |
Inglês |
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Conteúdo: |
This paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application usingLANDSATimages from MidwestBrazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time. |
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Palavras-Chave: |
Interpretação multitemporal; Lógica Fuzzy. |
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Thesagro: |
Sensoriamento Remoto. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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Marc: |
LEADER 01966naa a2200241 a 4500
001 1339184
005 2016-10-04
008 2007 bl uuuu u00u1 u #d
024 7 $a10.1016/j.isprsjprs.2007.04.001$2DOI
100 1 $aMOTA, G. L. A.
245 $aMultitemporal fuzzy classification model based on class transition possibilities.$h[electronic resource]
260 $c2007
520 $aThis paper proposes a new method to model temporal knowledge and to combine it with spectral and spatial knowledge within an integrated fuzzy automatic image classification framework for land-use land-cover map update applications. The classification model explores not only the object features, but also information about its class at a previous date. The method expresses temporal class dependencies by means of a transition diagram, assigning a possibility value to each class transition. A Genetic Algorithm (GA) carries out the class transition possibilities estimation. Temporal and spectral/spatial classification results are combined by means of fuzzy aggregation. The improvement achieved by the use of multitemporal knowledge rather than a pure monotemporal approach was assessed in a real application usingLANDSATimages from MidwestBrazil. The experiments showed that the use of temporal knowledge markedly improved the classification performance, in comparison to a conventional single-time classification. A further observation was that multitemporal knowledge may subsume the knowledge related to steady spatial attributes whose values do not significantly change over time.
650 $aSensoriamento Remoto
653 $aInterpretação multitemporal
653 $aLógica Fuzzy
700 1 $aFEITOSA, R. Q.
700 1 $aCOUTINHO, H. L. C.
700 1 $aLIEDTKE, C. E.
700 1 $aMÜLLER, S
700 1 $aPAKZAD, K.
700 1 $aMEIRELLES, M. S. P.
773 $tISPRS Journal of Photogrammetry & Remote Sensing, Amsterdam$gv. 62, n. 3, p. 186-200, Aug. 2007.
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| 2. |  | GILES, A. L.; FREITAS, M. G.; ESPADA, A. L. V.; CONSTANTINO, P.; FRANCISCO, B. S.; RODRIGUES, S. B.; SAMPAIO, A. B.; FREITAS, I. F. de; BUENO, G. C. S.; LACERDA, R. C. A.; EDSON JÚNIOR, R.; SILVIUS, K.; VIEIRA, D. L. M. Brazil’s science–policy effort to monitor native vegetation restoration. Ambio, 2026. On-line first.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
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