Generalização de modelos digitais de terreno com base em transformada wavelet

Gaboardi, Clovis; Mitishita, Edson Aparecido; Firkowski, Henrique

doi:10.1590/s1982-21702011000100007

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…The diverse spatial distribution of measurement points does not always ensure the uniform coverage of the surface being measured. In the measurement spectrum, there may be areas of varying measurement point density and unintentional data gaps, causing shortages and voids in the resource being acquired [55,56]. Moreover, due to the need to identify the points located on the topographic surface, a proportion of the measurement information is filtered out.…”

Section: Introductionmentioning

confidence: 99%

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Gościewski,

Gerus-Gościewska,

Szczepańska

2024

Remote Sensing

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Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model.

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Section: Introductionmentioning

confidence: 99%

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Gościewski,

Gerus-Gościewska,

Szczepańska

2024

Remote Sensing

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“…This enables the collection of very large volumes of data in a relatively short time. However, due to their quantity, spatial distribution and specific characteristics, such datasets cannot, as a rule, be used directly in the creation of a DTM [18][19][20][21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Adjusting the Regular Network of Squares Resolution to the Digital Terrain Model Surface Shape

Gościewski

Gerus-Gościewska

2020

IJGI

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A regular network of squares is formed by points uniformly distributed (mostly in the square corners) over the surface that is represented by the network. Each point (node) of the network has specified coordinates (X and Y) with a fixed constant distance between them. The third coordinate in a node (H) is determined by the application of interpolation based on the points distributed (usually dispersed as a point cloud e.g., from LiDAR) over the surface of the area surrounding the node. The regular network of squares formed in this manner allows the representation of a digital terrain model (DTM) to be performed in spatial information systems (SIP, GIS). The main problem that arises during the construction of such a network is the proper determination of its resolution (the base distance between the coordinates X and Y) depending on the topography. This article presents a method of the regular network of squares resolution determination depending on the morphological shape of the terrain surface. Following the application of the procedures being described, a differently shaped terrain is assigned various network densities. This enables the minimisation of inaccuracies of the surface model being formed. Consequently, a regular network of squares is formed with different base square sizes, which is adjusted with its resolution to the morphology of the surface it describes. Such operations allow the terrain model accuracy to be maintained over the entire area while reducing the number of points stored in the DTM database to the minimum.

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Predicting the Number of DCT Coefficients in the Process of Seabed Data Compression

Forczmański

Maleika

2015

Computer Analysis of Images and Patterns

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Generalização de modelos digitais de terreno com base em transformada wavelet

Cited by 7 publications

References 13 publications

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Application of Polynomial Interpolation for Iterative Complementation of the Missing Nodes in a Regular Network of Squares Used for the Construction of a Digital Terrain Model

Adjusting the Regular Network of Squares Resolution to the Digital Terrain Model Surface Shape

Predicting the Number of DCT Coefficients in the Process of Seabed Data Compression

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