  Optical-SAR image matching is a fundamental task for image fusion and visual
navigation. However, all large-scale open SAR dataset for methods development
are collected from single platform, resulting in limited satellite types and
spatial resolutions. Since images captured by different sensors vary
significantly in both geometric and radiometric appearance, existing methods
may fail to match corresponding regions containing the same content. Besides,
most of existing datasets have not been categorized based on the
characteristics of different scenes. To encourage the design of more general
multi-modal image matching methods, we introduce a large-scale
Multi-sources,Multi-resolutions, and Multi-scenes dataset for Optical-SAR image
matching(3MOS). It consists of 155K optical-SAR image pairs, including SAR data
from six commercial satellites, with resolutions ranging from 1.25m to 12.5m.
The data has been classified into eight scenes including urban, rural, plains,
hills, mountains, water, desert, and frozen earth. Extensively experiments show
that none of state-of-the-art methods achieve consistently superior performance
across different sources, resolutions and scenes. In addition, the distribution
of data has a substantial impact on the matching capability of deep learning
models, this proposes the domain adaptation challenge in optical-SAR image
matching. Our data and code will be available at:https://github.com/3M-OS/3MOS.
