2012
DOI: 10.1016/j.jas.2012.02.022
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Mapping by matching: a computer vision-based approach to fast and accurate georeferencing of archaeological aerial photographs

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Cited by 194 publications
(138 citation statements)
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References 26 publications
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“…The software was chosen as it has been proven to be effective in the production of dense and accurate point clouds over forest areas [9]. Photoscan offers a user-friendly processing pipeline that combines proprietary algorithms from computer vision SfM and stereo-matching algorithms to accomplish the tasks of image alignment and multiview stereo-reconstruction [46]. In our study, an intermediate step of camera alignment optimization was carried out using accurately measured GCPs.…”
Section: Photogrammetric Processingmentioning
confidence: 99%
“…The software was chosen as it has been proven to be effective in the production of dense and accurate point clouds over forest areas [9]. Photoscan offers a user-friendly processing pipeline that combines proprietary algorithms from computer vision SfM and stereo-matching algorithms to accomplish the tasks of image alignment and multiview stereo-reconstruction [46]. In our study, an intermediate step of camera alignment optimization was carried out using accurately measured GCPs.…”
Section: Photogrammetric Processingmentioning
confidence: 99%
“…This procedure finds the optimal borders between the images and creates seams based on those borders, requiring an image overlapping of 40 to 70 %. Mosaicking minimizes misalignments, which are especially visible in borders of two contiguous images (Wan et al, 2013); the main advantages of this include image spatial continuity, allowing for coastline location on longer coastal stretches and facilitating the search for common ground control points (GPCs) (Verhoeven et al, 2012). Mosaics were georeferenced using GCPs that corresponded to common elements in both datasets, evenly distributed along the coastline.…”
Section: Mapping Proceduresmentioning
confidence: 99%
“…The working principles of SfM are similar to those of stereoscopic photogrammetry, namely that the 3D model can be created from overlapping, offset images. However, unlike traditional photogrammetry, in which either the position of the camera or the positions of some points are known prior to scene reconstruction (Fonstad et al, 2013;Verhoeven et al, 2012;Westoby et al, 2012), in the SfM, matches are made between points across many photographs without prior knowledge of the camera position (Lowe, 2004).…”
Section: Surface Elevation Changes Through Structure-from-motionmentioning
confidence: 99%
“…The images acquired were processed using the commercial software Agisoft PhotoScan®, as already successfully considered in different analyses (Doneus et al, 2011;Javernick et al, 2014;Piermattei et al, 2016;Prosdocimi et al, 2015;Verhoeven et al, 2012;Woodget et al, 2015). A custom algorithm similar to the Lowe's (2004) Scale Invariant Feature Transform (SIFT) object recognition system was used by the software to determine the 3D location of matching features in multiple images.…”
Section: Surface Elevation Changes Through Structure-from-motionmentioning
confidence: 99%
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