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Data fusion is a well-known technique, becoming more and more popular in theArtificial Intelligence for Earth Observation (AI4EO) domain mainly due to itsability of reinforcing AI4EO applications by combining multiple data sourcesand thus bringing better results. On the other hand, like other methods forsatellite data analysis, data fusion itself is also benefiting and evolvingthanks to the integration of Artificial Intelligence (AI). In this letter, fourdata fusion paradigms, based on Convolutional Neural Networks (CNNs), areanalyzed and implemented. The goals are to provide a systematic procedure forchoosing the best data fusion framework, resulting in the best classificationresults, once the basic structure for the CNN has been defined, and to helpinterested researchers in their work when data fusion applied to remote sensingis involved. The procedure has been validated for land-cover classification butit can be transferred to other cases.

Paradigm selection for Data Fusion of SAR and Multispectral Sentinel data applied to Land-Cover Classification