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The joint interpretation of very high resolution SAR and optical images indense urban area are not trivial due to the distinct imaging geometry of thetwo types of images. Especially, the inevitable layover caused by theside-looking SAR imaging geometry renders this task even more challenging. Onlyuntil recently, the "SARptical" framework [1], [2] proposed a promisingsolution to tackle this. SARptical can trace individual SAR scatterers incorresponding high-resolution optical images, via rigorous 3-D reconstructionand matching. This paper introduces the SARptical dataset, which is a datasetof over 10,000 pairs of corresponding SAR, and optical image patches extractedfrom TerraSAR-X high-resolution spotlight images and aerial UltraCAM opticalimages. This dataset opens new opportunities of multisensory data analysis. Onecan analyze the geometry, material, and other properties of the imaged objectin both SAR and optical image domain. More advanced applications such as SARand optical image matching via deep learning [3] is now also possible.

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