Elevation Model Mosaicking From Coregistration, Adjustment, and Median of Stacks (CAMS)

A large and growing volume of repeat digital elevation models (DEMs) obtained from multiple spaceborne sensors enables high-resolution mapping of the Earth’s surface at continental scales. Mosaicking of individual DEMs to form a continuous surface is challenging due to variability data quality and positional accuracy, all of which can result in artifacts. Presented is a method for efficiently mosaicking sets of repeat, overlapping DEMs using their pairwise, translational offsets to remove poor quality DEMs and optimize their alignment prior to merging. The coregistration, adjustment, and median of stacks (CAMS) approach is tested by mosaicking a set of 2-m resolution DEMs created from WorldView (WV) stereoscopic imagery and comparing the result to light detection and ranging (LiDAR) data. CAMS produces a mosaic of substantially higher quality and accuracy than that obtained from the median of all overlapping DEMS, as commonly performed for mosaicking satellite-derived DEMs. The method requires no sensor-specific information or ground control, making it applicable for large-area mosaic production using multiple datasets.