A Novel Method Based on Source Domain Understanding and Modeling to Transfer Labels from Land-Cover Vector Maps to Classifiers for Multispectral Images

Combining existing thematic vector products and recently acquired satellite images to generate regular updated maps is extremely interesting at operational level. However, employing these maps is not straightforward. They are typically provided at polygon level, where the polygon labels do not necessarily correspond to spectrally homogeneous areas. Moreover, usually there is a semantic gap between the map legend and the set of natural classes discriminable in multispectral images. To overcome these issues, this paper presents a method that first performs a domain understanding to detect the discrepancies between the vector map domain and the multispectral (MS) image domain. Then, it accomplishes a domain modeling which uses a MS image contemporary to the map to extract a set of reliable and informative samples from the map. Finally, the method carries out Domain Adaptation (DA) using a recent MS image to update the map. Experimental results obtained updating a crop thematic map in Czech Republic confirm the effectiveness of the method.