# The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area

arXiv: https://arxiv.org/abs/1801.07532 · HTML: https://mlsanity.com/paper/1801.07532/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2018.

## Built on by (most cited first, 8 of 8)

- [The QXS-SAROPT Dataset for Deep Learning in SAR-Optical Data Fusion](https://mlsanity.com/paper/2103.08259/) (2021)
- [Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation](https://mlsanity.com/paper/2408.07947/) (2024)
- [Multi-Resolution SAR and Optical Remote Sensing Image Registration Methods: A Review, Datasets, and Future Perspectives](https://mlsanity.com/paper/2502.01002/) (2025)
- [3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching](https://mlsanity.com/paper/2404.00838/) (2024)
- [Advances and Challenges in Multimodal Remote Sensing Image Registration](https://mlsanity.com/paper/2302.00912/) (2023)
- [CrossEarth-SAR: A SAR-Centric and Billion-Scale Geospatial Foundation Model for Domain Generalizable Semantic Segmentation](https://mlsanity.com/paper/2603.12008/) (2026)
- [SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and A Progressive Learning Strategy for Downstream Tasks](https://mlsanity.com/paper/2507.18743/) (2025)
- [LoRetta: A Foundation Model and Extensive Dataset for Global-Scale Remote Sensing Dense Image Matching](https://mlsanity.com/paper/2608.04106/) (2026)

## Abstract (arXiv)

The joint interpretation of very high resolution SAR and optical images in dense urban area are not trivial due to the distinct imaging geometry of the two types of images. Especially, the inevitable layover caused by the side-looking SAR imaging geometry renders this task even more challenging. Only until recently, the "SARptical" framework [1], [2] proposed a promising solution to tackle this. SARptical can trace individual SAR scatterers in corresponding high-resolution optical images, via rigorous 3-D reconstruction and matching. This paper introduces the SARptical dataset, which is a dataset of over 10,000 pairs of corresponding SAR, and optical image patches extracted from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical images. This dataset opens new opportunities of multisensory data analysis. One can analyze the geometry, material, and other properties of the imaged object in both SAR and optical image domain. More advanced applications such as SAR and optical image matching via deep learning [3] is now also possible.