  Deep learning (DL) methods are widely investigated for stereo image matching
tasks due to their reported high accuracies. However, their
transferability/generalization capabilities are limited by the instances seen
in the training data. With satellite images covering large-scale areas with
variances in locations, content, land covers, and spatial patterns, we expect
their performances to be impacted. Increasing the number and diversity of
training data is always an option, but with the ground-truth disparity being
limited in remote sensing due to its high cost, it is almost impossible to
obtain the ground-truth for all locations. Knowing that classical stereo
matching methods such as Census-based semi-global-matching (SGM) are widely
adopted to process different types of stereo data, we therefore, propose a
finetuning method that takes advantage of disparity maps derived from SGM on
target stereo data. Our proposed method adopts a simple scheme that uses the
energy map derived from the SGM algorithm to select high confidence disparity
measurements, at the same utilizing the images to limit these selected
disparity measurements on texture-rich regions. Our approach aims to
investigate the possibility of improving the transferability of current DL
methods to unseen target data without having their ground truth as a
requirement. To perform a comprehensive study, we select 20 study-sites around
the world to cover a variety of complexities and densities. We choose
well-established DL methods like geometric and context network (GCNet), pyramid
stereo matching network (PSMNet), and LEAStereo for evaluation. Our results
indicate an improvement in the transferability of the DL methods across
different regions visually and numerically.
