  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.
