  The advances in remote sensing technologies have boosted applications for
Earth observation. These technologies provide multiple observations or views
with different levels of information. They might contain static or temporary
views with different levels of resolution, in addition to having different
types and amounts of noise due to sensor calibration or deterioration. A great
variety of deep learning models have been applied to fuse the information from
these multiple views, known as deep multi-view or multi-modal fusion learning.
However, the approaches in the literature vary greatly since different
terminology is used to refer to similar concepts or different illustrations are
given to similar techniques. This article gathers works on multi-view fusion
for Earth observation by focusing on the common practices and approaches used
in the literature. We summarize and structure insights from several different
publications concentrating on unifying points and ideas. In this manuscript, we
provide a harmonized terminology while at the same time mentioning the various
alternative terms that are used in literature. The topics covered by the works
reviewed focus on supervised learning with the use of neural network models. We
hope this review, with a long list of recent references, can support future
research and lead to a unified advance in the area.
