Translating SAR to Optical Images for Assisted Interpretation
This addresses the challenge of making SAR images more accessible for general users by enabling assisted interpretation, though it is incremental as it builds on existing GAN methods for image translation.
The paper tackled the problem of translating SAR images to optical versions to assist interpretation, proposing a reciprocal GAN scheme that works well across various scenarios, as demonstrated on GF-3 and UAVSAR datasets.
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.