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27 june 2019 16:03:55

 
Remote Sensing, Vol. 11, Pages 1526: Modelling the Surface of Racing Vessel`s Hull by Laser Scanning and Digital Photogrammetry (Remote Sensing)
 


The knowledge of the hull shape and geometry of a racing vessel is one of the most important factors for predicting boat performance. The Offshore Racing Congress (ORC) rating system specifies the calculation parameters of the hydrodynamic forces of boat lift and drag on the basis of input data as the length of waterline while sailing, displacement, wetted surface and the volume distribution along the hull. It is represented by sophisticated calculations for national as well as international events and races. Measurement using a reflectorless total station in a coordinate system defined by the sailboat hull is the most established method approved by the ORC organisation. The determination of these geometric parameters by new, unconventional technologies, which should provide a quicker and more detailed measurement while preserving the quality and accuracy of results necessary for the handicap calculations was our main objective. Geometrical shapes of a cabin sailboat hull were determined by the technology of terrestrial laser scanning and two methods of digital close-range photogrammetry—convergence case of photogrammetry and Structure-from-Motion (SfM) method. High-Definition Surveying (HDS) targets for laser scanning and coded targets for digital photogrammetry were used throughout all methods in order to transform the resulting data into a single local coordinate system. The resulting models were mutually compared by visual, geometrical and statistical comparison. In conclusion, both technologies were considered suitable, however, with various advantages and disadvantages. Nevertheless, although labour intensive, the SfM photogrammetry can be considered the most suitable method if the correct procedures are followed.


 
54 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 1527: A Simple Similarity Index for the Comparison of Remotely Sensed Time Series with Scarce Simultaneous Acquisitions (Remote Sensing)
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