  The Corona KH-4 reconnaissance satellite missions from 1962-1972 acquired
panoramic stereo imagery with high spatial resolution of 1.8-7.5 m. The
potential of 800,000+ declassified Corona images has not been leveraged due to
the complexities arising from handling of panoramic imaging geometry, film
distortions and limited availability of the metadata required for
georeferencing of the Corona imagery. This paper presents Corona Stereo
Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic
imagery. CoSP utlizes a deep learning based feature matcher SuperGlue to
automatically match features point between Corona KH-4 images and recent
satellite imagery to generate Ground Control Points (GCPs). To model the
imaging geometry and the scanning motion of the panoramic KH-4 cameras, a
rigorous camera model consisting of modified collinearity equations with time
dependent exterior orientation parameters is employed. The results show that
using the entire frame of the Corona image, bundle adjustment using
well-distributed GCPs results in an average standard deviation (SD) of less
than 2 pixels. The distortion pattern of image residuals of GCPs and y-parallax
in epipolar resampled images suggest that film distortions due to long term
storage as likely cause of systematic deviations. Compared to the SRTM DEM, the
Corona DEM computed using CoSP achieved a Normalized Median Absolute Deviation
(NMAD) of elevation differences of ~4 m over an area of approx. 4000 $km^2$. We
show that the proposed pipeline can be applied to sequence of complex scenes
involving high relief and glacierized terrain and that the resulting DEMs can
be used to compute long term glacier elevation changes over large areas.
