  Image-to-image (I2I) translation is an established way of translating data
from one domain to another but the usability of the translated images in the
target domain when working with such dissimilar domains as the SAR/optical
satellite imagery ones and how much of the origin domain is translated to the
target domain is still not clear enough. This article address this by
performing translations of labelled datasets from the optical domain to the SAR
domain with different I2I algorithms from the state-of-the-art, learning from
transferred features in the destination domain and evaluating later how much
from the original dataset was transferred. Added to this, stacking is proposed
as a way of combining the knowledge learned from the different I2I translations
and evaluated against single models.
