  Cross-view image matching aims to match images of the same target scene
acquired from different platforms. With the rapid development of drone
technology, cross-view matching by neural network models has been a widely
accepted choice for drone position or navigation. However, existing public
datasets do not include images obtained by drones at different heights, and the
types of scenes are relatively homogeneous, which yields issues in assessing a
model's capability to adapt to complex and changing scenes. In this end, we
present a new cross-view dataset called SUES-200 to address these issues.
SUES-200 contains 24120 images acquired by the drone at four different heights
and corresponding satellite view images of the same target scene. To the best
of our knowledge, SUES-200 is the first public dataset that considers the
differences generated in aerial photography captured by drones flying at
different heights. In addition, we developed an evaluation for efficient
training, testing and evaluation of cross-view matching models, under which we
comprehensively analyze the performance of nine architectures. Then, we propose
a robust baseline model for use with SUES-200. Experimental results show that
SUES-200 can help the model to learn highly discriminative features of the
height of the drone.
