Deep nets can be used efficiently to deal with multiple heterogeneous sources, for instance, in implementing a number of branches tailored for each data source, that eventually merge together at some point to fuse the information. For example, it is easy to build a deep neural network that can deal simultaneously with time series and one very-high-resolution image: while one branch composed of a recurrent neural network processes the time series (For instance Sentinel-1 or Sentinel-2 images), another branch composed of a CNN processes the high-resolution image (for instance Spot 6/7 images). Such approaches are available in the literature (for instance in [13]).
This section will present how OTBTF can be ...
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