SAR-to-EO Image Translation with Multi-Conditional Adversarial Networks

Autor:	Cabrera, Armando, Cha, Miriam, Sharma, Prafull, Newey, Michael
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Image and Video Processing
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.13184 Zobrazit plný text záznamu View this record from Arxiv