Accuracy Assessment of Thematic Maps

Thematic Accuracy

Learning Objectives

• What is accuracy assessment and why is it crucially important?

• What is the typical procedure for assessing thematic accuracy of maps?

• What are reference data, and how are they collected?

• What is contingency table analysis, and how do you calculate the common accuracy metrics?

• As a map maker, what can you learn from accuracy assessment?

What is Accuracy Assessment?

Map Accuracy -- The proportion of agreement between a classified map and reference data assumed to be correct.

Thematic Precision – The level of detail that is mapped. A map distinguishing lodgepole pine, Douglas fir, Ponderosa, etc. is more precise (but probably less accurate!) than a map just showing Forest.

Accuracy and precision are DIFFERENT!

Spatial Accuracy vs. Thematic Accuracy

• Thematic accuracy is how well the class names on the map correspond to what is really on the ground.

• Spatial accuracy quantifies errors in the locations of boundaries.

• Thematic and spatial accuracy are related but are usually treated separately.

Steps for performing thematic accuracy assessment:

1) Develop a sampling scheme2) Collect reference data3) Compare reference data to classified map4) Compute accuracy metrics and deliver to

map users

Sampling Schemes

• Reference locations must be unbiased• Reference locations must be large enough to

find with certainty on your classified image• You must ensure that you can correctly

identify the types at your reference sites• You must visit LOTS of reference locations

Reference data collection sites for 2001 NLCD map, with ecoregions.

Collecting Reference Data

• Field collection– Use GPS to find pre-determined reference sites– Use explicit definitions of the classes to insure that

reference data are consistent (should have done this with map classes too)

– Develop efficient plan for visiting sites and contingencies for unreachable sites

National Park Service image (nps.org)

Collecting Reference Data (cont.)

• Double Sampling – Using high resolution remotely sensed data to assess the accuracy of low resolution data– Air photos, Air videography, higher resolution satellites,

etc.– Requires some ground work to “train” yourself to

accurately interpret the reference data– May negate the assumption of accurate reference data!– Relatively inexpensive, good for inaccessible areas, fast,

etc.

Air Videography

Quantifying Accuracy – Comparing Mapped Types to Reference Types

• Contingency Tables = Error Matrices = Confusion Matrices

• Traditional Accuracy Statistics are calculated from the error matrix

Contingency Table or Error Matrix

• Error matrix consists of n x n array where n is the number of classes

• Rows: reference (“correct”) data (n rows)• Columns: mapped classes (n cols)

Error Analyses - Example

• Create a map with 3 thematic classes (water, forest and urban)

• Collect 95 ground reference data– (Water 33, Forest 39 and Urban 23)

• Compare those locations to those places in the map

• Generate Error Matrix.

Contingency Table

Reference data

Classified image

Water Forest Urban Total

Water 21 5 7 33

Forest 6 31 2 39

Urban 0 1 22 23

Total 27 37 31 95

Classification Accuracy

• Lots of ways to look at the thematic accuracy of a classification– Overall accuracy– Errors of omission– Errors of commission– User’s accuracy– Producer’s accuracy– Accuracy statistics (e.g., Kappa)– Fuzzy accuracy

Overall Accuracy

• Of all of the reference sites, what proportion were mapped correctly?

• Easiest to understand but least amount of information for map users and map producers (us).

Overall Accuracy

Reference data

Classified image

Water Forest Urban Total

Water 21 5 7 33

Forest 6 31 2 39

Urban 0 1 22 23

Total 27 37 31 95

Correctly classified:21 + 31 + 22 = 74

Total number reference sites = 95

Overall accuracy = 74 / 95 = 77.9%

Off-diagonal Elements

• The off-diagonal elements of a contingency table tell us the most about how to improve our remote sensing classification!– Should spend lots of time examining ERRORS to

figure out what went wrong

Errors of Omission• The type on the ground is not that type on

the classified image – the real type is OMITTED from the classified image.

Omission Error

Reference data

Classified image

Water Forest Urban Total

Water 21 5 7 33

Forest 6 31 2 39

Urban 0 1 22 23

Total 27 37 31 95

For water:5 + 7 = 1212 / 33 = 36%

For forest:6 + 2 = 88 / 39 = 20%

For urban:0 + 1 = 11 / 23 = 4%

Errors of Commission• A type on the classified image is not that

type on the ground – the type is COMMITTED to the classified image.

Commission Error

Reference data

Classified image

Water Forest Urban Total

Water 21 5 7 33

Forest 6 31 2 39

Urban 0 1 22 23

Total 27 37 31 95

For water:6 + 0 = 66 / 27 = 22%

For forest:5 + 1 = 66 / 37 = 16%

For urban:7 + 2 = 99 / 31 = 29%

Producer’s Accuracy

• Map accuracy from the point of view of the map maker (PRODUCER).

• How often are real features on the ground correctly shown on the map?

Producer’s AccuracyCan be computed (and reported) for each thematic

classProducer accuracy = 100 – omission error

Water: 100 – 36 = 64% Forest: 100 – 20 = 80%Urban: 100 – 4 = 96%

Or PA = #correct/row total

User’s Accuracy

• Accuracy from the point of view of a map USER (not a map maker).

• How often is the type the map says should be there really there?

User’s Accuracy

Can be computed (and reported) for each thematic class

User accuracy = 100 – commission error

Water: 100 – 22 = 78% Forest: 100 – 16 = 84%Urban: 100 – 29 = 71%

Or UA = #correct/column total

Kappa Statistic

• A measure of how accurate your map is above and beyond the accuracy expected by chance alone

• Considers omission, commission, and overall accuracy

• Frequently reported in the literature and used as a common way to compare maps

Kappa Statistic

Kappa = (observed – expected)/(1 – expected)

Observed is the overall accuracy from your contingency table (the sum of the diagonal)/(Total reference pixels)

Expected is calculated from a matrix of “marginal products”Create a matrix by multiplying marginal totals.

Expected = diagonal sum/total sum (from the marginal table)

(See Campbell for calculation details)

Fuzzy Accuracy• Recognizes that binary accuracy assessment

(right vs. wrong) discards important information

• Can use scale of “rightness” or “wrongness” to get more nuanced accuracy assessment (e.g. absolutely right, good, reasonable, mostly wrong, absolutely wrong)

• There are many fuzzy metrics that can capture this information numerically.

Accuracy Assessment -- Summary

• Absolutely critical to any remote sensing based mapping project

• Expensive – should be included in the budget from the start

• Requires collection of accurate reference data either in the field or from higher resolution data

• Analysis should include many aspects of accuracy to give users more information about the product.