Digital Elevation Model‐Assisted Aerial Triangulation Method On An Unmanned Aerial Vehicle Sweeping Camera System

Abstract

The sweeping camera systems in the surveying and mapping industry are usually efficient in image acquisition, for the photography coverage of a single strip is relatively large. The triangulation angle of correspondence is overly tiny to adopt traditional block adjustment (BA). This study analysed the imaging principle of the Chinese APS7K comprehensive camera system and proposed an aerial triangulation method for the data this system acquired. The proposed method first determines the adjacent matrix from the POS data and trajectory information. The other part of the method is to overcome the weak relative geometry in a single strip by introducing the digital elevation model (DEM) data into the block adjustment scheme. The optimal solution of adjustment is obtained by iteratively solving the problem. We verified the optimisation's effectiveness by checking stitched orthophoto and check points from Google Earth. The results show that mosaic discrepancy is eliminated, the reprojection error is reduced to subpixel level, and positioning accuracy is better than 0.4 meter (ground sample distance is 0.2 meter) after adjustment with ground control points. Finally, the method’s shortcomings and prospects are summarized.

Digital surface model generation from aerial imagery using bridge probability relaxation matching

“…For each feature f detected in L1, search for its conjugate point f′ in R1 using the gray template matching method. To avoid matching outliers and save computation time, use the elements of exterior orientation and average ground elevation to predict the position of its conjugate point and limit the initial search range 39 . In template matching, the NCC is used to calculate the similarity of two pixels.…”

“…To avoid matching outliers and save computation time, use the elements of exterior orientation and average ground elevation to predict the position of its conjugate point and limit the initial search range. 39 In template matching, the NCC is used to calculate the similarity of two pixels. According to the winner-take-all scheme, 40 the point with the highest similarity is regarded as the matching point.…”

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