메뉴 건너뛰기




Volumn 5, Issue , 2017, Pages 1-10

Exploring Google earth engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping

Author keywords

Big data; Classification; Google Earth Engine; Image Processing; Land cover; Land use; Optical satellite imagery

Indexed keywords

AGRICULTURAL ROBOTS; BIG DATA; CROPS; DATA HANDLING; DECISION TREES; EFFICIENCY; ENGINES; FOOD SUPPLY; IMAGE CLASSIFICATION; IMAGE PROCESSING; LAND USE; MAPPING; NEURAL NETWORKS; OPTICAL DATA PROCESSING; REMOTE SENSING; SATELLITE IMAGERY; SUPPORT VECTOR MACHINES;

EID: 85019930085     PISSN: None     EISSN: 22966463     Source Type: Journal    
DOI: 10.3389/feart.2017.00017     Document Type: Article
Times cited : (334)

References (36)
  • 2
    • 84894288250 scopus 로고    scopus 로고
    • Deriving crop specific covariate data sets from multi-year NASS geospatial cropland data layers
    • Melbourne, VIC
    • Boryan, C. G., and Yang, Z. (2013, July). “Deriving crop specific covariate data sets from multi-year NASS geospatial cropland data layers,” in 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS (Melbourne, VIC), 4225-4228.
    • (2013) 2013 IEEE International Geoscience and Remote Sensing Symposium-Igarss , pp. 4225-4228
    • Boryan, C.G.1    Yang, Z.2
  • 3
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • Breiman, L. (2001). Random forests. Mach. Learn. 45, 5-32. doi: 10.1023/A:1010933404324
    • (2001) Mach. Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 4
    • 0026278621 scopus 로고
    • A review of assessing the accuracy of classifications of remotely sensed data
    • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37, 35-46. doi: 10.1016/0034-4257(91)90048-B
    • (1991) Remote Sens. Environ , vol.37 , pp. 35-46
    • Congalton, R.G.1
  • 8
    • 84886045513 scopus 로고    scopus 로고
    • Accuracy, objectivity and efficiency of remote sensing for agricultural statistics
    • eds R. Benedetti, M. Bee, G. Espa, and F. Piersimoni (Chichester, UK: JohnWiley & Sons, Ltd
    • Gallego, J., Carfagna, E., and Baruth, B. (2010). “Accuracy, objectivity and efficiency of remote sensing for agricultural statistics,” in Agricultural Survey Methods, eds R. Benedetti, M. Bee, G. Espa, and F. Piersimoni (Chichester, UK: JohnWiley & Sons, Ltd.). doi: 10.1002/9780470665480.ch12
    • (2010) Agricultural Survey Methods
    • Gallego, J.1    Carfagna, E.2    Baruth, B.3
  • 9
    • 84864540336 scopus 로고    scopus 로고
    • Efficiency assessment of different approaches to crop classification based on satellite and ground observations
    • Gallego, J., Kravchenko, A. N., Kussul, N. N., Skakun, S. V., Shelestov, A. Y., and Grypych, Y. A. (2012). Efficiency assessment of different approaches to crop classification based on satellite and ground observations. J. Autom. Inf. Sci. 44, 67-80. doi: 10.1615/JAutomatInfScien.v44.i5.70
    • (2012) J. Autom. Inf. Sci , vol.44 , pp. 67-80
    • Gallego, J.1    Kravchenko, A.N.2    Kussul, N.N.3    Skakun, S.V.4    Shelestov, A.Y.5    Grypych, Y.A.6
  • 10
    • 33746932125 scopus 로고    scopus 로고
    • On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance
    • Gao, F., Masek, J., Schwaller, M., and Hall, F. (2006). On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Trans. Geosci. Remote Sens. 44, 2207-2218. doi: 10.1109/TGRS.2006.872081
    • (2006) IEEE Trans. Geosci. Remote Sens , vol.44 , pp. 2207-2218
    • Gao, F.1    Masek, J.2    Schwaller, M.3    Hall, F.4
  • 12
    • 67651113818 scopus 로고    scopus 로고
    • Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model
    • Hilker, T., Wulder, M. A., Coops, N. C., Seitz, N., White, J. C., Gao, F., et al. (2009). Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. Remote Sens. Environ. 113, 1988-1999. doi: 10.1016/j.rse.2009.05.011
    • (2009) Remote Sens. Environ , vol.113 , pp. 1988-1999
    • Hilker, T.1    Wulder, M.A.2    Coops, N.C.3    Seitz, N.4    White, J.C.5    Gao, F.6
  • 13
    • 84880317742 scopus 로고    scopus 로고
    • Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models
    • Kogan, F., Kussul, N., Adamenko, T., Skakun, S., Kravchenko, O., Kryvobok, O., et al. (2013a). Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models. Int. J. Appl. Earth Observ. Geoinform. 23, 192-203. doi: 10.1016/j.jag.2013.01.002
    • (2013) Int. J. Appl. Earth Observ. Geoinform , vol.23 , pp. 192-203
    • Kogan, F.1    Kussul, N.2    Adamenko, T.3    Skakun, S.4    Kravchenko, O.5    Kryvobok, O.6
  • 14
    • 84883343032 scopus 로고    scopus 로고
    • Winter wheat yield forecasting: A comparative analysis of results of regression and biophysical models
    • Kogan, F., Kussul, N. N., Adamenko, T. I., Skakun, S. V., Kravchenko, A. N., Krivobok, A. A., et al. (2013b). Winter wheat yield forecasting: A comparative analysis of results of regression and biophysical models. J. Autom. Inf. Sci. 45, 68-81. doi: 10.1615/JAutomatInfScien.v45.i6.70
    • (2013) J. Autom. Inf. Sci , vol.45 , pp. 68-81
    • Kogan, F.1    Kussul, N.N.2    Adamenko, T.I.3    Skakun, S.V.4    Kravchenko, A.N.5    Krivobok, A.A.6
  • 18
    • 84930335603 scopus 로고    scopus 로고
    • Geospatial Intelligence and Data Fusion Techniques for Sustainable Development Problems
    • Kussul, N., Shelestov, A., Basarab, R., Skakun, S., Kussul, O., and Lavrenyuk, M. (2015). “Geospatial Intelligence and Data Fusion Techniques for Sustainable Development Problems,” in ICTERI (Lviv), 196-203.
    • (2015) ICTERI (Lviv) , pp. 196-203
    • Kussul, N.1    Shelestov, A.2    Basarab, R.3    Skakun, S.4    Kussul, O.5    Lavrenyuk, M.6
  • 19
    • 84872056550 scopus 로고    scopus 로고
    • Grid technologies for satellite data processing and management within international disaster monitoring projects
    • eds S. Fiore and G. Aloisio (Berlin; Heidelberg: Springer
    • Kussul, N., Shelestov, A., and Skakun, S. (2011). “Grid technologies for satellite data processing and management within international disaster monitoring projects,” in Grid and Cloud Database Management, eds S. Fiore and G. Aloisio (Berlin; Heidelberg: Springer), 279-305.
    • (2011) Grid and Cloud Database Management , pp. 279-305
    • Kussul, N.1    Shelestov, A.2    Skakun, S.3
  • 21
    • 34250091945 scopus 로고
    • Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm
    • Littlestone, N. (1988). Learning quickly when irrelevant attributes abound: a new linear-threshold algorithm. Mach. Learn. 2, 285-318. doi: 10.1007/BF00116827
    • (1988) Mach. Learn , vol.2 , pp. 285-318
    • Littlestone, N.1
  • 22
    • 84930826671 scopus 로고    scopus 로고
    • Towards building a dataintensive index for big data computing-a case study of Remote Sensing data processing
    • Ma, Y., Wang, L., Liu, P., and Ranjan, R. (2015a). Towards building a dataintensive index for big data computing-a case study of Remote Sensing data processing. Inf. Sci. 319, 171-188. doi: 10.1016/j.ins.2014.10.006
    • (2015) Inf. Sci , vol.319 , pp. 171-188
    • Ma, Y.1    Wang, L.2    Liu, P.3    Ranjan, R.4
  • 23
    • 84930575472 scopus 로고    scopus 로고
    • Remote sensing big data computing: Challenges and opportunities
    • Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R., Zomaya, A., et al. (2015b). Remote sensing big data computing: challenges and opportunities. Future Generation Comput. Syst. 51, 47-60. doi: 10.1016/j.future.2014. 10.029
    • (2015) Future Generation Comput. Syst , vol.51 , pp. 47-60
    • Ma, Y.1    Wu, H.2    Wang, L.3    Huang, B.4    Ranjan, R.5    Zomaya, A.6
  • 25
    • 69849110324 scopus 로고    scopus 로고
    • Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories
    • McNairn, H., Champagne, C., Shang, J., Holmstrom, D. A., and Reichert, G. (2009). Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS J. Photogramm. Remote Sens. 64, 434-449. doi: 10.1016/j.isprsjprs.2008.07.006
    • (2009) ISPRS J. Photogramm. Remote Sens , vol.64 , pp. 434-449
    • McNairn, H.1    Champagne, C.2    Shang, J.3    Holmstrom, D.A.4    Reichert, G.5
  • 26
    • 0028183911 scopus 로고
    • SMAC: A simplified method for the atmospheric correction of satellite measurements in the solar spectrum
    • Rahman, H., and Dedieu, G. (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Remote Sens. 15, 123-143. doi: 10.1080/01431169408954055
    • (1994) Remote Sens , vol.15 , pp. 123-143
    • Rahman, H.1    Dedieu, G.2
  • 27
    • 44149102637 scopus 로고    scopus 로고
    • Multitemporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data
    • Roy, D. P., Ju, J., Lewis, P., Schaaf, C., Gao, F., Hansen, M., et al. (2008). Multitemporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sens. Environ. 112, 3112-3130. doi: 10.1016/j.rse.2008.03.009
    • (2008) Remote Sens. Environ , vol.112 , pp. 3112-3130
    • Roy, D.P.1    Ju, J.2    Lewis, P.3    Schaaf, C.4    Gao, F.5    Hansen, M.6
  • 28
    • 84896818071 scopus 로고    scopus 로고
    • Landsat-8: Science and product vision for terrestrial global change research
    • Roy, D. P., Wulder, M. A., Loveland, T. R., Woodcock, C. E., Allen, R. G., Anderson, M. C., et al. (2014). Landsat-8: science and product vision for terrestrial global change research. Remote Sens. Environ. 145, 154-172. doi: 10.1016/j.rse.2014.02.001
    • (2014) Remote Sens. Environ , vol.145 , pp. 154-172
    • Roy, D.P.1    Wulder, M.A.2    Loveland, T.R.3    Woodcock, C.E.4    Allen, R.G.5    Erson, M.C.6
  • 29
    • 79952748054 scopus 로고    scopus 로고
    • Pegasos: Primal estimated sub-gradient solver for svm
    • Shalev-Shwartz, S., Singer, Y., Srebro, N., and Cotter, A. (2011). Pegasos: primal estimated sub-gradient solver for svm. Math. Programming 127, 3-30. doi: 10.1007/s10107-010-0420-4
    • (2011) Math. Programming , vol.127 , pp. 3-30
    • Shalev-Shwartz, S.1    Singer, Y.2    Srebro, N.3    Cotter, A.4
  • 30
    • 84961210112 scopus 로고    scopus 로고
    • The use of satellite data for agriculture drought risk quantification in Ukraine
    • Skakun, S., Kussul, N., Shelestov, A., and Kussul, O. (2016b). The use of satellite data for agriculture drought risk quantification in Ukraine. Geomatics Nat. Hazards Risk, 7, 901-917. doi: 10.1080/19475705.2015.1016555
    • (2016) Geomatics Nat. Hazards Risk , vol.7 , pp. 901-917
    • Skakun, S.1    Kussul, N.2    Shelestov, A.3    Kussul, O.4
  • 31
    • 84938815954 scopus 로고    scopus 로고
    • Efficiency assessment of multitemporal C-Band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine
    • Skakun, S., Kussul, N., Shelestov, A. Y., Lavreniuk, M., and Kussul, O. (2016a). Efficiency assessment of multitemporal C-Band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 9, 3712-3719. doi: 10.1109/JSTARS.2015.2454297
    • (2016) IEEE J. Select. Top. Appl. Earth Observ. Remote Sens , vol.9 , pp. 3712-3719
    • Skakun, S.1    Kussul, N.2    Shelestov, A.Y.3    Lavreniuk, M.4    Kussul, O.5
  • 32
    • 84920511605 scopus 로고    scopus 로고
    • Reconstruction of missing data in timeseries of optical satellite images using self-organizing Kohonen maps
    • Skakun, S. V., and Basarab, R. M. (2014). Reconstruction of missing data in timeseries of optical satellite images using self-organizing Kohonen maps. J. Autom. Inf. Sci. 46, 19-26. doi: 10.1615/JAutomatInfScien.v46.i12.30
    • (2014) J. Autom. Inf. Sci , vol.46 , pp. 19-26
    • Skakun, S.V.1    Basarab, R.M.2
  • 33
    • 34249997303 scopus 로고    scopus 로고
    • Analysis of applicability of neural networks for classification of satellite data
    • Skakun, S. V., Nasuro, E. V., Lavrenyuk, A. N., and Kussul, O. M. (2007). Analysis of applicability of neural networks for classification of satellite data. J. Autom. Inf. Sci. 39, 37-50. doi: 10.1615/JAutomatInfScien.v39.i3.40
    • (2007) J. Autom. Inf. Sci , vol.39 , pp. 37-50
    • Skakun, S.V.1    Nasuro, E.V.2    Lavrenyuk, A.N.3    Kussul, O.M.4
  • 34
    • 84982913053 scopus 로고    scopus 로고
    • Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity
    • Waldner, F., De Abelleyra, D., Verón, S. R., Zhang, M., Wu, B., Plotnikov, D., et al. (2016). Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity. Int. J. Remote Sens. 37, 3196-3231. doi: 10.1080/01431161.2016.1194545
    • (2016) Int. J. Remote Sens , vol.37 , pp. 3196-3231
    • Waldner, F.1    De Abelleyra, D.2    Verón, S.R.3    Zhang, M.4    Wu, B.5    Plotnikov, D.6
  • 35
    • 84893445992 scopus 로고    scopus 로고
    • Automated crop field extraction from multitemporal Web Enabled Landsat Data
    • Yan, L., and Roy, D. P. (2014). Automated crop field extraction from multitemporal Web Enabled Landsat Data. Remote Sens. Environ. 144, 42-64. doi: 10.1016/j.rse.2014.01.006
    • (2014) Remote Sens. Environ , vol.144 , pp. 42-64
    • Yan, L.1    Roy, D.P.2
  • 36
    • 83455195642 scopus 로고    scopus 로고
    • Object-based cloud and cloud shadow detection in Landsat imagery
    • Zhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83-94. doi: 10.1016/j.rse.2011.10.028
    • (2012) Remote Sens. Environ , vol.118 , pp. 83-94
    • Zhu, Z.1    Woodcock, C.E.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.