Review of Interpolation Methods for Digital Terrain Models

Schut, G. H.

doi:10.1139/tcs-1976-0037

Cited by 53 publications

(23 citation statements)

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“…The accuracy of a DEM that is produced with an interpolation procedure is related to the density and the distribution of the reference altitudes, as well as the selection of the interpolation procedure used (Schut 1976). Even the simplest interpolation method may be useful if the density of the reference altitudes is high and their distribution is ideal.…”

Section: Methods Of Inverse Distance Weighted (Idw) Interpolationmentioning

confidence: 99%

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The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

Achilleos

2011

Journal of Spatial Science

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Interpolation procedures are widely used in science, especially in sciences that involve spatial data and continuous phenomena that can be depicted on a continuous spatial surface. Interpolation makes use of accurate and qualitative sampling data in order to produce a continuous representation of the phenomenon in question. The accuracy of the data used for interpolation directly affects the results. This research examines error propagation within the Inverse Distance Weighted (IDW) method, applied as a means of representing the earth's relief. Interpolation of a DEM within contours on a topographical map is considered to be a three-stage procedure. The first stage is the digitising of the contours depicted on the analogue map. Errors involved in this stage are propagated to the second stage, the geometric transformation of coordinates of these digitised contours. Additional errors due to the application of the transformation model are embedded within the results thus obtained. Finally, the errors are propagated to the third stage. Errors are magnified due to the application of an interpolation method, in our case, the IDW method. Any stage of the procedure can be considered separately. The procedure aims to cover the situation where a DEM is created using an analogue topographical map in conjunction with the IDW interpolation method.

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Section: Methods Of Inverse Distance Weighted (Idw) Interpolationmentioning

confidence: 99%

“…The Inverse Distance Weighted (IDW) method is widely recognised as the basic method in most systems that create and manage DEMs (Burrough 1986;Schut 1976).…”

Section: Inverse Distance Weighted (Idw) Methodsmentioning

confidence: 99%

The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

Achilleos

2011

Journal of Spatial Science

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“…A regular grid of ground-based index values was interpolated using a technique called prediction (or krieging) [11].…”

Section: Processing Methodsmentioning

confidence: 99%

Automatic monitoring of autumn colours using MODIS data

Rauste

Astola

Häme

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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An automatic methodology was developed for monitoring of the progress of autumn colours for the needs of tourists and tourism industry. MODIS imagery of red and near-infrared bands were used. Cloud problems were alleviated aggregating pixel blocks of 280 lines by 280 columns (70 km by 70 km). The drop of near-infrared reflectance from its summer-time maximum was used as a measure of the autumn colourization state. Satellite based autumn colour index was combined to a trend surface derived from ground observations.

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“…Hansen and Vögtle 1999;Axelsson 2000); (3) methods using algorithms based on iterative matching of the modelled area with the data representing the real surface of the area (e.g. Schut 1976;Kass, Witkin, and Terzopoulos 1988;Kraus and Pfeifer 1998;Elmqvist 2002); and (4) methods involving segmentation of the point cloud into several classes, e.g. terrain surface, buildings, plant cover (e.g.…”

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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Review of Interpolation Methods for Digital Terrain Models

Cited by 53 publications

References 0 publications

The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

Automatic monitoring of autumn colours using MODIS data

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

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Review of Interpolation Methods for Digital Terrain Models

Cited by 53 publications

References 0 publications

The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

The Inverse Distance Weighted interpolation method and error propagation mechanism – creating a DEM from an analogue topographical map

Automatic monitoring of autumn colours using MODIS data

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

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