A novel automatic approach to the update of land-cover maps by unsupervised classification of remote sensing images

This paper presents an approach to the update of land-cover maps by classifying Remote Sensing (RS) images in an unsupervised way. The proposed method assumes that: i) an old thematic map is available; ii) no ground truth data are available; iii) the source used to generate the available thematic map is unknown. To classify the most recent RS image available on the considered area, the method automatically extracts from the considered land-cover map a “pseudo” training set. First, a preprocessing phase adapts the map to the properties of the RS data. Then, we perform an automatic “pseudo” training set identification to select the most reliable samples from the existing thematic map. Finally, a consistency check is defined to determine whether the inconsistencies between the updated and the original maps are due to real changes on the ground or classification errors. Experimental results obtained by updating the 2012 Corine Land Cover Map (CLC) in Trentino, Italy, using Sentinel 2 (S2) images confirm the effectiveness of the proposed method.