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Volumn 2016-November, Issue , 2016, Pages 7145-7148

Along the season crop classification in Ukraine based on time series of optical and SAR images using ensemble of neural network classifiers

Author keywords

crop map; Early season classification; neural network; satellite data

Indexed keywords


EID: 85007433730     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IGARSS.2016.7730864     Document Type: Conference Paper
Times cited : (16)

References (17)
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    • Kussul, N.1    Skakun, S.2    Shelestov, A.3    Kravchenko, O.4    Gallego, J.F.5    Kussul, O.6
  • 4
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    • Becker-Reshef, I.1    Vermote, E.2    Lindeman, M.3    Justice, C.4
  • 5
    • 84961210112 scopus 로고    scopus 로고
    • The use of satellite data for agriculture drought risk quantification in Ukraine
    • S. Skakun, N. Kussul, A. Shelestov and O. Kussul, "The use of satellite data for agriculture drought risk quantification in Ukraine," Geomatics, Natural Hazards and Risk, 2015, doi: 10.1080/19475705.2015.1016555
    • (2015) Geomatics, Natural Hazards and Risk
    • Skakun, S.1    Kussul, N.2    Shelestov, A.3    Kussul, O.4
  • 6
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    • Shao, Y.1    Lunetta, R.S.2
  • 7
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    • An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series
    • N. Matton, G. S. Canto, F. Waldner, S. Valero, D. Morin, J. Inglada, and P. Defourny, "An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series," Remote Sensing, vol. 7, no. 10, pp. 13208-13232, 2015
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    • Matton, N.1    Canto, G.S.2    Waldner, F.3    Valero, S.4    Morin, D.5    Inglada, J.6    Defourny, P.7
  • 11
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    • Efficiency assessment of different approaches to crop classification based on satellite and ground observations
    • J. Gallego, A. Kravchenko, N. Kussul, S. Skakun, A. Shelestov, and Y. Grypych, "Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations," J. Autom. Inf. Sci., vol. 44, no. 5, pp. 67-80, 2012
    • (2012) J. Autom. Inf. Sci. , vol.44 , Issue.5 , pp. 67-80
    • Gallego, J.1    Kravchenko, A.2    Kussul, N.3    Skakun, S.4    Shelestov, A.5    Grypych, Y.6
  • 13
    • 34249997303 scopus 로고    scopus 로고
    • Analysis of Applicability of Neural Networks for Classification of Satellite Data
    • S. V. Skakun, E. V. Nasuro, A. N. Lavrenyuk, and O. M. Kussul, "Analysis of Applicability of Neural Networks for Classification of Satellite Data," J. Autom. Inf. Sci., vol. 39, no. 3, pp. 37-50, 2007
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  • 15
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    • Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories
    • H. McNairn, C. Champagne, J. Shang, S. Holmstrom, and G. Reichert, "Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories," ISPRS J. Photogramm. And Remote Sens., vol. 64, no. 5, pp. 434-449, 2009
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  • 17
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    • S. Skakun, and R. Basarab, "Reconstruction of Missing Data in Time-Series of Optical Satellite Images Using Self-Organizing Kohonen Maps," J. Autom. Inf. Sci., vol. 46, no. 12, pp. 19-26, 2015.
    • (2015) J. Autom. Inf. Sci. , vol.46 , Issue.12 , pp. 19-26
    • Skakun, S.1    Basarab, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.