Difference between revisions of "HorizontalAccuracy"

Line

Component

Measure Description

1

Name

absolute circular error at 90 % significance level of biased data

2

Alias

CMAS

3

Data quality element

Absolute or External Positional Accuracy

4

Data quality basic measure

5

Definition

absolute horizontal accuracy of the data’s coordinates, expressed in terms of circular error at 90 % probability given that a bias is present

6

Description

See STANAG 2215 ed7 Appendix 2

7

Parameter

8

Data quality value type

Measure

9

Data quality value structure

10

Source reference

NATO STANAG 2215

ISO 19157 measure n°48

11

Example

12

Identifier

http://dgiwg.org/metadata/qualityMeasure/CMAS

Line

Component

Measure Description

1

Name

absolute circular error at 90 % significance level of biased data

2

Alias

ACE

3

Element name

absolute or external accuracy

4

Basic measure

not applicable

5

Definition

absolute horizontal accuracy of the data’s coordinates, expressed in terms of circular error at 90 % probability given that a bias is present

6

Description

A comparison of the data (source) and the control (reference) is calculated in the following manner:

1. Calculate the absolute error in the horizontal dimension at each point:

Δ H i = ( source X i − reference X i ) 2 + ( source Y i − reference Y i ) 2 for i = 1…N

2. Calculate the mean horizontal error:

μ H = ( ∑ Δ H i ) N

3. Calculate the standard deviation of the horizontal errors:

σ H = ∑ ( Δ H i − μ H ) 2 ( N − 1 )

4. Calculate the ratio of the absolute value of the mean error to the standard deviation:

ratio = | μ H | / σ H

5. If ratio > 1 , 4 , then k = 1 , 281   5

6. If ratio ≤ 1,4, then calculate k, the ratio of the mean to the standard deviation, using a cubic polynomial fit through the tabular values as defined in the CRC Handbook of Tables for Probability and Statistics [ 20 ]

k = 1 , 643   5 − ( 0 , 999   556 × ratio ) + ( 0 , 923   237 × ratio 2 ) − ( 0 , 282   533 × ratio 3 )

7. Compute CE90 for the source:

CE90 source = | μ H | + ( k × σ H )

8. Compute absolute CE90:

CE90 abs = CE90 reference 2 + CE90 source 2

7

Parameter

Name: Sample size

Definition: minimum of 30 points is normally used but may not always be possible depending on identifiable control points. For feature level attribution sample 10 % of the feature population.

Value Type: Real

8

Value type

Measure

9

Value structure

-

10

Source reference

ISO 19157 Measure n° 49

1. Mapping, Charting and Geodesy Accuracy (Reference[ 21 ])

2. Handbook of Tables for Probability and Statistics (Reference[ 20 ])

11

Example

-

12

Identifier

http://dgiwg.org/metadata/qualityMeasure/ACE

Line

Component

Measure Description

1

Name

Cartography of quality zones

2

Alias

CQZ

3

Data quality element

Absolute or External Positional Accuracy

4

not applicable

5

Definition

Cartography of horizontal quality zones (defined by their boundaries in GML or Shapefile)

6

Description

Information related to the result of a quality evaluation of the dataset on absolute horizontal accuracy (CE90) covResult :

- format = GML (version 3.3) or SHP (version 1.0)

- geometry = surface

- content = (contentType=’qualityInformation’)

- file = (name=’link to the GML or SHP file)

- and (description=”Cartography of quality zones”)

7

Parameter

8

Data quality value type

GM_Surface

9

Data quality value structure

10

Source reference

http://dgiwg.org/metadata/qualityMeasure/ACE

11

Example

12

Identifier

http://www.dgiwg.org/metadata/qualityMeasure/CQZ

Line

Component

Measure Description

1

Name

Point-to-point horizontal accuracy

2

Alias

RelCE90

3

Data quality element

Relative or internal accuracy

4

Data quality basic measure

5

Definition

Uncertainty in the difference in horizontal positions between any 2 points. The value is expressed as a circular error at the 90% confidence level.

6

Description

See STANAG 2215 ed7 Appendix 2

7

Parameter

8

Data quality value type

Measure

9

Data quality value structure

10

Source reference

NATO STANAG 2215

11

Example

12

Identifier

http://dgiwg.org/metadata/qualityMeasure/RelCE90

Line

Component

Measure Description

1

Name

US National Image Interpretability Rating Scales

2

Alias

NIIRS

3

Data quality element

Relative or Internal accuracy

4

Data quality basic measure

5

Definition

The aerial imaging community utilizes the National Imagery Interpretability Rating Scale (NIIRS) to define and measure the quality of images and performance of imaging systems. Through a process referred to as "rating" an image, the NIIRS is used by imagery analysts to assign a number which indicates the interpretability of a given image. The NIIRS concept provides a means to directly relate the quality of an image to the interpretation tasks for which it may be used. Although the NIIRS has been primarily applied in the evaluation of aerial imagery, it provides a systematic approach to measuring the quality of photographic or digital imagery, the performance of image capture devices, and the effects of image processing algorithms.

6

Description

See https://fas.org/irp/imint/niirs.htm

7

Parameter

8

Data quality value type

Measure

9

Data quality value structure

10

Source reference

https://fas.org/irp/imint/niirs.htm

-

11

Example

12

Identifier

http://dgiwg.org/metadata/qualityMeasure/NIIRS