CLASSIFICATION OF SELECTED CORINE CLASSES USING SENTINEL-2 DATA

Abstract. This paper addresses the classification of CORINE classes. Three land use classes (arable land, pastures, and natural grassland) report similar spectral responses which make it challenging to separate. Therefore, we adopted a multitemporal and multispectral approach using Sentinel-2 satellite imagery in combination with the NDVI vegetation index, Haralick’s textural measures, and topographic information. The workflow identifies a methodology for combining various groups of input data (optical, NDVI, textural, topographic) and explores the suitable use of the Random Forest classifier for the task. The classification was carried out in three different European locations. The results present a strong separation of arable land (F1 score over 96%) from the other two classes. Pastures and natural grassland classes achieved F1 in the range of 76% to almost 85% in both cases. In conclusion, we discuss the suitability of the CORINE database for such classification problems.