  Although nowadays advanced dense image matching (DIM) algorithms are able to
produce LiDAR (Light Detection And Ranging) comparable dense point clouds from
satellite stereo images, the accuracy and completeness of such point clouds
heavily depend on the geometric parameters of the satellite stereo images. The
intersection angle between two images are normally seen as the most important
one in stereo data acquisition, as the state-of-the-art DIM algorithms work
best on narrow baseline (smaller intersection angle) stereos (E.g. Semi-Global
Matching regards 15-25 degrees as good intersection angle). This factor is in
line with the traditional aerial photogrammetry configuration, as the
intersection angle directly relates to the base-high ratio and texture
distortion in the parallax direction, thus both affecting the horizontal and
vertical accuracy. However, our experiments found that even with very similar
(and good) intersection angles, the same DIM algorithm applied on different
stereo pairs (of the same area) produced point clouds with dramatically
different accuracy as compared to the ground truth LiDAR data. This raises a
very practical question that is often asked by practitioners: what factors
constitute a good satellite stereo pair, such that it produces accurate and
optimal results for mapping purpose? In this work, we provide a comprehensive
analysis on this matter by performing stereo matching over 1,000 satellite
stereo pairs with different acquisition parameters including their intersection
angles, off-nadir angles, sun elevation & azimuth angles, as well as time
differences, thus to offer a thorough answer to this question. This work will
potentially provide a valuable reference to researchers working on multi-view
satellite image reconstruction, as well as industrial practitioners minimizing
costs for high-quality large-scale mapping.
