RT Journal Article ID 72f27fd82715649b A1 Gallego, Javier A1 Kravchenko, Alexey N. A1 Kussul, Nataliya N. A1 Skakun, Sergey V. A1 Shelestov, Andrey Yu. A1 Grypych, Yulia A. T1 Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations JF Journal of Automation and Information Sciences JO JAI(S) YR 2012 FD 2012-07-12 VO 44 IS 5 SP 67 OP 80 K1 crop classification K1 satellite and ground observations K1 efficiency assessment K1 different approaches to crop classification K1 different data levels K1 extremely large data. AB A problem of crop plants classification for three regions of Ukraine with an area of 78.500 km2 is considered. Classification is carried out using not a single satellite but a time series of satellite images. The used satellite data are characterized by different spatial resolution and temporal characteristics. By example of this problem we assessed the efficiency of different classification algorithms (neural networks, decision trees and support vector machines) for substantially different data levels (of training and testing samples) both for extremely large data sets and under the condition of their lack (absence). PB Begell House LK https://www.dl.begellhouse.com/journals/2b6239406278e43e,77eca4ec4b58fce7,72f27fd82715649b.html