  Despite the advantages of all-weather and all-day high-resolution imaging,
SAR remote sensing images are much less viewed and used by general people
because human vision is not adapted to microwave scattering phenomenon.
However, expert interpreters can be trained by compare side-by-side SAR and
optical images to learn the translation rules from SAR to optical. This paper
attempts to develop machine intelligence that are trainable with large-volume
co-registered SAR and optical images to translate SAR image to optical version
for assisted SAR interpretation. A novel reciprocal GAN scheme is proposed for
this translation task. It is trained and tested on both spaceborne GF-3 and
airborne UAVSAR images. Comparisons and analyses are presented for datasets of
different resolutions and polarizations. Results show that the proposed
translation network works well under many scenarios and it could potentially be
used for assisted SAR interpretation.
