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    Wyświetlanie 1-2 z 2
    Tytuł:
    Point cloud unification with optimization algorithm
    Autorzy:
    Błaszczak-Bąk, W.
    Sobieraj, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/298162.pdf
    Data publikacji:
    2015
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    laser scanning
    unification
    optimization
    Opis:
    Terrestrial laser scanning is a technology that enables to obtain three-dimensional data - an accurate representation of reality. During scanning not only desired objects are measured, but also a lot of additional elements. Therefore, unnecessary data is being removed, what has an impact on efficiency of point cloud processing. It can happen while single point clouds are displayed - user decides what he wants to deleted and does it manually, or by using tools provided in dedicated for point cloud processing softwares. In Leica Geosystems Cyclone - software used here in tests, user can apply tools e.g. for merging or unification of point clouds. Both of them change the separate points clouds into one points cloud, however unification can be executed with reduction - low, medium, high, highest or no reduction at all. It should be noted, that the modeled objects may have complex structure and unification with selected type of reduction can have a very big impact on the result of modeling. In such situation it is desirable to apply different types of reduction. In this article authors propose to apply an optimization algorithm on unified point clouds. Unification conducted by means of Cyclone Leica Geosystems (v.7.3.3) enables to merge point clouds and reduced the number of points. The point elimination is determined mainly by spacing between points. It may leads to loose of important points - representing some essential elements of scanned objects or area. Applying optimization algorithm, especially for complex objects, may help to reduce the number of points without losing the information necessary for proper modeling.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2015, 18(4); 271-282
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Impact of optimization of ALS point cloud on classification
    Autorzy:
    Błaszczak-Bąk, W.
    Sobieraj, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/298363.pdf
    Data publikacji:
    2013
    Wydawca:
    Uniwersytet Warmińsko-Mazurski w Olsztynie
    Tematy:
    optimization
    classification
    intensity
    Opis:
    Airborne laser scanning (ALS) is one of the LIDAR technologies (Light Detection and Ranging). It provides information about the terrain in form of a point cloud. During measurement is acquired: spatial data (object’s coordinates X, Y, Z) and collateral data such as intensity of reflected signal. The obtained point cloud is typically applied for generating a digital terrain model (DTM) and a digital surface model (DSM). For DTM and DSM generation it is necessary to apply filtration or classification algorithms. They allow to divide a point cloud into object groups (e.g.: terrain points, vegetation, etc.). In this study classification is conducted with one extra parameter–intensity. The obtained point groups were used for digital spatial model generation. Classification is a time and work consuming process, therefore there is a need to reduce the time of ALS point cloud processing. Optimization algorithm enables to decrease the number of points in a dataset. In this study the main goal was to test the impact of optimization on the results of a classification. Studies were conducted in two variants. Variant 1 includes classification of the original point cloud where points are divided in the groups: roofs, asphalt road, tree/bushes, grass. On variant 2 before classification, an optimization algorithm was performed in the original point cloud. Obtained from these two variants object groups were used to generate a spatial model, which was then statistically analyzed.
    Źródło:
    Technical Sciences / University of Warmia and Mazury in Olsztyn; 2013, 16(2); 147-164
    1505-4675
    2083-4527
    Pojawia się w:
    Technical Sciences / University of Warmia and Mazury in Olsztyn
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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