Influence of Block Geometry Configuration on Multi-Image Dense Matching

Abstract

Digital photogrammetry is a widespread surveying technique in different fields of application due to its flexibility, versatility and cost-effectiveness. Despite its increasing automation and simplicity, a proper image block design is crucial to ensure high standards of performance and accuracy. Studies on camera network design have been largely dealt with in the scientific literature with reference to image orientation process, while they are still poor on dense matching. This paper investigates the influence of different block geometry configurations on multi-image dense matching. Starting from the same orientation solution, dense matching was performed considering different combinations of number of images and base length distance between the first and the last image within a strip. The raster Digital Elevation Models (DEM) resulting from each sequence of images were compared with a reference DEM to assess accuracy and completeness. The tests were conducted using different cameras and at various test sites to assess different survey conditions and generalize the findings. The presented results provide some operational guidance on block geometry optimization to maximize the accuracy and completeness.

New Trends in High-Resolution Imagery Processing

“…Some remote sensing techniques and procedures that can be considered consolidated are still being studied and investigated more deeply; operations of fundamental interest for the proper use of satellite imagery are constantly evolving to improve procedural efficiency and the accuracy of results; among these, we can mention the fusion of images acquired from different sensors [3] and pansharpening-a technique that allows combining spectral information with the higher geometric accuracy of panchromatic images-an operation of fundamental importance for many feature extraction processes [4,5]. Similarly, the extraction of elevation information from stereo pairs and triple acquisitions (tri-image acquisition) requires specific in-depth analysis to optimize workflows [6][7][8]. The reverse problem of observing the satellite orbit from the ground can also be implemented with a complete and portable system for image acquisition [9].…”