2018
DOI: 10.1080/00396265.2018.1474685
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Reduction of measurement data before Digital Terrain Model generation vs. DTM generalisation

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Cited by 4 publications
(4 citation statements)
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“…The time required for the implementation of the OptD method can be considered as negligible in the whole process of preparing the data for the DTM construction. In the work, [35] has shown that the approach based on the OptD method is less time-and labour-consuming than the approach based on DTM generalization. It results from the fact that before the DTM is generalized, it must first be built from the original point cloud.…”
Section: Theoretical Background Of Optd Methodsmentioning
confidence: 99%
“…The time required for the implementation of the OptD method can be considered as negligible in the whole process of preparing the data for the DTM construction. In the work, [35] has shown that the approach based on the OptD method is less time-and labour-consuming than the approach based on DTM generalization. It results from the fact that before the DTM is generalized, it must first be built from the original point cloud.…”
Section: Theoretical Background Of Optd Methodsmentioning
confidence: 99%
“…As optimization criteria in the OptD method parameters like: the number of points in reduced dataset (M) and the percentage of points to be in the dataset after processing (p%) were used and tested so far [13][14][15][16][17]. In this paper it was decided to use the standard deviation estimator (SD) of ALS data.…”
Section: Data Reduction and Dtm Generationmentioning
confidence: 99%
“…In this paper focus is on the influence of the source data, in particular, whether the standard deviation estimator of ALS (Airborne Laser Scanning) data can be used as optimization criterion in dataset reduction and whether using SD has an impact on the generated DTM. Reduction was performed by means of the Optimum Dataset (OptD) method [12], which allows to preserve points representing characteristics elements in reduced dataset [13,14,15]. DTM was generated on the basis of the original dataset (after its filtration) as well as from the datasets obtained after processing by the OptD method.…”
Section: Introductionmentioning
confidence: 99%
“…It can be used in the OptD-single variant, when there is one optimization criterion, or in the OptD-multi one when there are more criteria. The OptD-single method was tested, among others, in [1,9], while the OptD-multi method in [2]. In the case of processing by means of the OptD-single method, one solution is obtained, while the OptD-multi gives as a result more than one solution.…”
Section: Introductionmentioning
confidence: 99%