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

Abstract

Along the season crop classification based on satellite data is challenging task for Ukraine because of a big diversity of different agricultural crops with different phenology (crop calendars). Taking into account the availability for free of high resolution (10 to 30 meter) optical and SAR data from different satellite, the most resource consuming task is ground data collecting. That is why the proper time of ground surveys and crop classification maps developing is very important. In the study we propose to build three crop classification maps for JECAM Ukraine test site in Kyiv region during the vegetation season. The first one is built in the middle of May to classify winter cereals and rapeseeds. The next crop classification map is developing in July to discriminate major summer crops (spring cereals, maize, soybeans, sunflowers). The final crop map is built in autumn to refine summer crops and sugar beet discrimination. Time series of multi-temporal satellite images with restored missing (clouded and shadowed) data are classified using neural network approach, in particular ensemble of multi-layer perceptrons (MLPs). It is shown, that addition of satellite data from the end of previous year to the spring imagery allows to significantly improve the accuracy of winter crops classification. In July it is possible to deliver the map with major summer crops with overall accuracy higher than 87%, and the overall accuracy of final map at the end of the season is 94%.