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Volumn , Issue , 2015, Pages

Improved crop classification using multitemporal RapidEye data

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL MACHINERY; AGRICULTURE; CROPS; CULTIVATION; DECISION TREES; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; LAND USE; MAXIMUM LIKELIHOOD; REMOTE SENSING; SUPPORT VECTOR MACHINES;

EID: 84959917567     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/Multi-Temp.2015.7245780     Document Type: Conference Paper
Times cited : (9)

References (14)
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  • 2
    • 84871261267 scopus 로고    scopus 로고
    • Mapping of summer crops in the state of parana, Brazil, through the 10-day spot vegetation ndvi composites
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    • (2011) Engenharia Agrcola , vol.2 , Issue.4 , pp. 760-770
    • Araujo, G.K.D.1    Rocha, J.V.2    Lamparelli, R.A.C.3    Rocha, A.M.4
  • 3
    • 84883544681 scopus 로고    scopus 로고
    • Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the hyspiri mission
    • I. Mariotto, P. S. Thenkabail, A. Huete, E. T. Slonecker, and A. Platonov, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the hyspiri mission, Remote Sensing of Environment, vol. 139, no. 0, pp. 291-305, 2013.
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  • 4
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    • November
    • U. Alganci, E. Sertel, M. Ozdogan, and C. Ormeci, Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern turkey, Photogrammetric Engineering and Remote Sensing, vol. 79, no. 11, pp. 1053-1065, November 2013.
    • (2013) Photogrammetric Engineering and Remote Sensing , vol.79 , Issue.11 , pp. 1053-1065
    • Alganci, U.1    Sertel, E.2    Ozdogan, M.3    Ormeci, C.4
  • 9
    • 84890026289 scopus 로고    scopus 로고
    • Comparision of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification
    • I. Nitze, U. Schulthess, and H. Asche, Comparision of machine learning algorithms random forest, artificial neural network and support vector machine to maximum likelihood for supervised crop type classification, in Proceedings of the 4th GEOBIA, Rio de Janeiro-Brazil, 2012, pp. 35-40.
    • (2012) Proceedings of the 4th GEOBIA, Rio de Janeiro-Brazil , pp. 35-40
    • Nitze, I.1    Schulthess, U.2    Asche, H.3
  • 12
    • 84880413388 scopus 로고    scopus 로고
    • The performance of random forests in an operational settingfor large area sclerophyll forest classification
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  • 13
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.