# Reciprocal Translation between SAR and Optical Remote Sensing Images with Cascaded-Residual Adversarial Networks

arXiv: https://arxiv.org/abs/1901.08236 · HTML: https://mlsanity.com/paper/1901.08236/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [Generative Artificial Intelligence Meets Synthetic Aperture Radar: A Survey](https://mlsanity.com/paper/2411.05027/) (2024)
- [Text-Guided Coarse-to-Fine Fusion Network for Robust Remote Sensing Visual Question Answering](https://mlsanity.com/paper/2411.15770/) (2024)
- [Differentiable SAR Renderer and SAR Target Reconstruction](https://mlsanity.com/paper/2205.07099/) (2022)
- [Accelerating Diffusion for SAR-to-Optical Image Translation via Adversarial Consistency Distillation](https://mlsanity.com/paper/2407.06095/) (2024)
- [SAR to Optical Image Translation with Color Supervised Diffusion Model](https://mlsanity.com/paper/2407.16921/) (2024)
- [TSGAN: An Optical-to-SAR Dual Conditional GAN for Optical based SAR Temporal Shifting](https://mlsanity.com/paper/2401.00440/) (2023)
- [SAR-to-Optical Image Translation via Thermodynamics-inspired Network](https://mlsanity.com/paper/2305.13839/) (2023)
- [SAR-to-RGB Translation with Latent Diffusion for Earth Observation](https://mlsanity.com/paper/2504.11154/) (2025)
- [DehazeMamba: SAR-guided Optical Remote Sensing Image Dehazing with Adaptive State Space Model](https://mlsanity.com/paper/2503.13073/) (2025)
- [Unpaired Object-Level SAR-to-Optical Image Translation for Aircraft with Keypoints-Guided Diffusion Models](https://mlsanity.com/paper/2503.19798/) (2025)
- [Domain Adaptive SAR Wake Detection: Leveraging Similarity Filtering and Memory Guidance](https://mlsanity.com/paper/2509.12279/) (2025)

## Abstract (arXiv)

Despite the advantages of all-weather and all-day high-resolution imaging, synthetic aperture radar (SAR) 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 comparing side-by-side SAR and optical images to learn the mapping 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 image interpretation. Reciprocal SAR-Optical image translation is a challenging task because it is raw data translation between two physically very different sensing modalities. This paper proposes a novel reciprocal adversarial network scheme where cascaded residual connections and hybrid L1-GAN loss are employed. It is trained and tested on both spaceborne GF-3 and airborne UAVSAR images. Results are presented for datasets of different resolutions and polarizations and compared with other state-of-the-art methods. The FID is used to quantitatively evaluate the translation performance. The possibility of unsupervised learning with unpaired SAR and optical images is also explored. Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.