Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud

Błaszczak-Bąk, Wioleta; Janowski, Artur; Kamiński, Waldemar; Rapiński, Jacek

doi:10.5589/m12-001

Cited by 19 publications

(12 citation statements)

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“…Both variants can be used to reduce the size of a set. The reduced sets, obtained by using selected methods (for reductionalgorithm (Błaszczak et al, 2011a), for generatingkriging method) are different in number of points in the set and its spatial distribution. Also characteristic of these sets as well as DMTs generated based on them are different.…”

Section: Discussionmentioning

confidence: 99%

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Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Błaszczak-Bąk¹

2016

Acta Geodynamica Et Geomaterialia

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The number of point clouds provided by LiDAR technology can be sometimes seen as a problem in development and further processing for given purposes (e.g. Digital Terrain Model (DTM) generation). Therefore, there is still a need to reduce the obtained big datasets. Reducing can be done, inter alia, by reducing the size of the set or by generating the set. This paper presents two variants of the reduction of point clouds in order to effectively generate the DTM. There is also a comparison of two DTMs generated on the basis of sets reduced by various methods. ARTICLE INFO

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Section: Discussionmentioning

confidence: 99%

“…Reduction decreases the size of dataset by removing some points according to given algorithm, remaining points are original points from measurement (Błaszczak, 2006;Błaszczak et al, 2011aBłaszczak et al, , 2011bChen, 2012).…”

Section: Reductionmentioning

confidence: 99%

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Błaszczak-Bąk¹

2016

Acta Geodynamica Et Geomaterialia

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“…The preliminary processing stage includes, among others, point cloud filtering, classification (segmentation), and optimization. Optimization is understood here as a reduction of the number of observations used for DTM creation without the loss of data necessary in this process (see, for instance, Błaszczak-Bąk et al 2011). The DTM is generated in the third stage, called the main processing.…”

Section: Introductionmentioning

confidence: 99%

Application of the M_splitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models

Błaszczak-Bąk

Janowski

Kamiński

et al. 2015

International Journal of Remote Sensing

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ALS point cloud filtering involves the separation of observations representing the physical terrain surface from those representing terrain details. A digital terrain model (DTM) is created from a subset of points representing the ground surface. The accuracy of the generated DTM is influenced by several factors, including the survey method used, the accuracy of the source data, the applied DTM generation algorithm, and the survey conditions. This article proposes the use of a new estimation method in the filtering of point clouds obtained from airborne laser scanning (ALS), provisionally called M splitestimation. The application of M split -estimation in ALS data filtering requires the determination of the appropriate functional model for the surface, which will be used in the filtering of the set of points. A polynomial terrain surface model was selected for this purpose. Two methods of filtering using the M split method are presented. The first is based on the estimated parameters of the polynomial describing the surface (called the 'quality' approach in the article). The second method (provisionally called the 'quantity' method) is carried out in two stages. The first stage is point cloud filtering, which results in two subsets being created. One of these is the subset of points intended for DTM creation, while the other contains the remaining points. The second stage of the approach is the creation of a DTM from the first subset.Since the M split method has an analytical character, the ATIN method was selected to verify the correct operation of the method. The ATIN method is based on computational geometry and uses repeated Delaunay triangulation and statistical evaluation of the geometric parameters. Comparison of M split with a method based on different principles mitigates errors arising from similarly functioning methods belonging to the same group of filters. The choice of the ATIN method was also dictated by its established position among filtering algorithms. The method is well-known, documented, and verified and this ensures that filtering by this method provides a reliable result that can serve as a reference for comparison with the proposed new filtering method.The theoretical discussion presented in this article was verified with two practical examples. The results obtained from computation by the M split method with appropriate terrain models encourage more detailed theoretical and empirical tests of this method for the filtering and segmentation of ALS data-sets.

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“…In this method we have new points instead of points with the original coordinates [2,11]. While reduction decreases the dataset by removing some points according to the given algorithm, the remaining points are original points from the measurement [4,5,6,9]. For people using data in the form of point clouds, it is better and easier to use real data.…”

Section: Introductionmentioning

confidence: 99%

The Optimum Dataset method – examples of the application

2018

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Abstract. Data reduction is a procedure to decrease the dataset in order to make their analysis more effective and easier. Reduction of the dataset is an issue that requires proper planning, so after reduction it meets all the user's expectations. Evidently, it is better if the result is an optimal solution in terms of adopted criteria. Within reduction methods, which provide the optimal solution there is the Optimum Dataset method (OptD) proposed by Błaszczak-Bąk (2016). The paper presents the application of this method for different datasets from LiDAR and the possibility of using the method for various purposes of the study. The following reduced datasets were presented: (a) measurement of Sielska street in Olsztyn (Airbrone Laser Scanning data -ALS data), (b) measurement of the bas-relief that is on the building in Gdańsk (Terrestrial Laser Scanning data -TLS data), (c) dataset from Biebrza river measurment (TLS data).

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Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud

Cited by 19 publications

References 2 publications

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Application of the M_splitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models

The Optimum Dataset method – examples of the application

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Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud

Cited by 19 publications

References 2 publications

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Influence of datasets decreased by applying reduction and generation methods on Digital Terrain Models

Application of the Msplitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models

The Optimum Dataset method – examples of the application

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Application of the M_splitmethod for filtering airborne laser scanning data-sets to estimate digital terrain models