Computation of Random Errors in Digital Terrain Models
Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure t...
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Gespeichert in:
| Autor*in: |
Bjørke, Jan T. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2007 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2007 |
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| Übergeordnetes Werk: |
Enthalten in: Geoinformatica - Springer US, 1997, 11(2007), 3 vom: 13. Feb., Seite 359-382 |
|---|---|
| Übergeordnetes Werk: |
volume:11 ; year:2007 ; number:3 ; day:13 ; month:02 ; pages:359-382 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10707-006-0012-x |
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OLC203896176X |
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| 520 | |a Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. | ||
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10.1007/s10707-006-0012-x doi (DE-627)OLC203896176X (DE-He213)s10707-006-0012-x-p DE-627 ger DE-627 rakwb eng 550 VZ Bjørke, Jan T. verfasserin aut Computation of Random Errors in Digital Terrain Models 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. measurement errors semivariogram trend removal subdivision Nilsen, Stein aut Enthalten in Geoinformatica Springer US, 1997 11(2007), 3 vom: 13. Feb., Seite 359-382 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:11 year:2007 number:3 day:13 month:02 pages:359-382 https://doi.org/10.1007/s10707-006-0012-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_4318 GBV_ILN_4700 AR 11 2007 3 13 02 359-382 |
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10.1007/s10707-006-0012-x doi (DE-627)OLC203896176X (DE-He213)s10707-006-0012-x-p DE-627 ger DE-627 rakwb eng 550 VZ Bjørke, Jan T. verfasserin aut Computation of Random Errors in Digital Terrain Models 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. measurement errors semivariogram trend removal subdivision Nilsen, Stein aut Enthalten in Geoinformatica Springer US, 1997 11(2007), 3 vom: 13. Feb., Seite 359-382 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:11 year:2007 number:3 day:13 month:02 pages:359-382 https://doi.org/10.1007/s10707-006-0012-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_4318 GBV_ILN_4700 AR 11 2007 3 13 02 359-382 |
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10.1007/s10707-006-0012-x doi (DE-627)OLC203896176X (DE-He213)s10707-006-0012-x-p DE-627 ger DE-627 rakwb eng 550 VZ Bjørke, Jan T. verfasserin aut Computation of Random Errors in Digital Terrain Models 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. measurement errors semivariogram trend removal subdivision Nilsen, Stein aut Enthalten in Geoinformatica Springer US, 1997 11(2007), 3 vom: 13. Feb., Seite 359-382 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:11 year:2007 number:3 day:13 month:02 pages:359-382 https://doi.org/10.1007/s10707-006-0012-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_4318 GBV_ILN_4700 AR 11 2007 3 13 02 359-382 |
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10.1007/s10707-006-0012-x doi (DE-627)OLC203896176X (DE-He213)s10707-006-0012-x-p DE-627 ger DE-627 rakwb eng 550 VZ Bjørke, Jan T. verfasserin aut Computation of Random Errors in Digital Terrain Models 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. measurement errors semivariogram trend removal subdivision Nilsen, Stein aut Enthalten in Geoinformatica Springer US, 1997 11(2007), 3 vom: 13. Feb., Seite 359-382 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:11 year:2007 number:3 day:13 month:02 pages:359-382 https://doi.org/10.1007/s10707-006-0012-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_4318 GBV_ILN_4700 AR 11 2007 3 13 02 359-382 |
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10.1007/s10707-006-0012-x doi (DE-627)OLC203896176X (DE-He213)s10707-006-0012-x-p DE-627 ger DE-627 rakwb eng 550 VZ Bjørke, Jan T. verfasserin aut Computation of Random Errors in Digital Terrain Models 2007 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2007 Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. measurement errors semivariogram trend removal subdivision Nilsen, Stein aut Enthalten in Geoinformatica Springer US, 1997 11(2007), 3 vom: 13. Feb., Seite 359-382 (DE-627)223334499 (DE-600)1357836-4 (DE-576)307633454 1384-6175 nnns volume:11 year:2007 number:3 day:13 month:02 pages:359-382 https://doi.org/10.1007/s10707-006-0012-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO GBV_ILN_11 GBV_ILN_70 GBV_ILN_100 GBV_ILN_4318 GBV_ILN_4700 AR 11 2007 3 13 02 359-382 |
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Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. © Springer Science+Business Media, LLC 2007 |
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Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. © Springer Science+Business Media, LLC 2007 |
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Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. After average interpolating trend removal, the error is derived from the variogram of the residual data set. This method is compared with a procedure that uses triangulation and resampling of two overlapping DTMs for extracting the error component. Eighteen small areas measured with a multibeam echo sounder are selected for the comparison. In the experiment the two methods come out with similar values for the random error. A by-product of the experiment is the development of a model for the average noise reduction caused by resampling of triangle models (TINs). The method we apply for the trend computation has time complexity O(n). Since the noise computation only requires the part of the variogram close to the origin, it is possible to formulate the computation of the random error in DTMs in O(n) time. © Springer Science+Business Media, LLC 2007 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC203896176X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230503062813.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2007 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10707-006-0012-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC203896176X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10707-006-0012-x-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">550</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bjørke, Jan T.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Computation of Random Errors in Digital Terrain Models</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2007</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2007</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Geostatistical methods make it possible to estimate the random errors in digital terrain models (DTMs) from one single set of measurements. 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