부산대학교 도서관

The Corona KH-4 reconnaissance satellite missions acquired panoramic stereo imagery with high spatial resolution of 1.8–7.5 m from 1962 to 1972. The potential of 800000+ 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 article presents the Corona Stereo Pipeline (CoSP): A pipeline for processing of Corona KH-4 stereo panoramic imagery. CoSP utilizes the deep learning-based feature matcher SuperGlue to automatically match feature points 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. Using the entire frame of the Corona image, bundle adjustment with well-distributed GCPs results in an average standard deviation (SD) or $\sigma _{0}$ of less than two pixels. We evaluate fiducial marks on the Corona films and show that pre-processing the Corona images to compensate for film bending improves the 3-D reconstruction accuracy. The distortion pattern of image residuals of GCPs and $y$ -parallax in epipolar resampled images suggest that film distortions due to long-term storage likely cause systematic deviations of up to six pixels. Compared to the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), the Corona DEM computed using CoSP achieved a normalized median absolute deviation (NMAD) of elevation differences of $\approx $ 4 m over an area of approximately 4000 km2 after a tile-based fine coregistration of the DEMs. We further assess CoSP on complex scenes involving high relief and glacierized terrain and show that the resulting DEMs can be used to compute long-term glacier elevation changes over large areas.