SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks
It addresses the problem of enhancing image translation for remote sensing applications, but is incremental as it builds on existing adversarial network methods by adding modalities.
This paper tackles SAR-to-EO image translation by incorporating multiple complementary modalities like Google Maps and IR to improve performance, particularly in preserving sharp edges of manmade objects, and demonstrates effectiveness on datasets such as SEN12MS, DFC2020, and SpaceNet6.
This paper explores the use of multi-conditional adversarial networks for SAR-to-EO image translation. Previous methods condition adversarial networks only on the input SAR. We show that incorporating multiple complementary modalities such as Google maps and IR can further improve SAR-to-EO image translation especially on preserving sharp edges of manmade objects. We demonstrate effectiveness of our approach on a diverse set of datasets including SEN12MS, DFC2020, and SpaceNet6. Our experimental results suggest that additional information provided by complementary modalities improves the performance of SAR-to-EO image translation compared to the models trained on paired SAR and EO data only. To best of our knowledge, our approach is the first to leverage multiple modalities for improving SAR-to-EO image translation performance.