Provided by: grass-doc_8.4.0-1_all 
NAME
r.kappa - Calculates error matrix and kappa parameter for accuracy assessment of
classification result.
KEYWORDS
raster, statistics, classification
SYNOPSIS
r.kappa
r.kappa --help
r.kappa [-whm] classification=name reference=name [output=name] [title=string]
format=name [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-w
Wide report
132 columns (default: 80)
-h
No header in the report
-m
Print Matrix only
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
classification=name [required]
Name of raster map containing classification result
reference=name [required]
Name of raster map containing reference classes
output=name
Name for output file containing error matrix and kappa
If not given write to standard output
title=string
Title for error matrix and kappa
Default: ACCURACY ASSESSMENT
format=name [required]
Output format
Options: plain, json
Default: plain
plain: Plain text output
json: JSON (JavaScript Object Notation)
DESCRIPTION
r.kappa tabulates the error matrix of classification result by crossing classified map
layer with respect to reference map layer. Both overall kappa (accompanied by its
variance) and conditional kappa values are calculated. This analysis program respects the
current geographic region and mask settings.
r.kappa calculates the error matrix of the two map layers and prepares the table from
which the report is to be created. kappa values for overall and each classes are computed
along with their variances. Also percent of commission and omission error, total correct
classified result by pixel counts, total area in pixel counts and percentage of overall
correctly classified pixels are tabulated.
The report will be written to an output file which is in plain text format and named by
user at prompt of running the program. To obtain machine readable version, specify a json
output format.
The body of the report is arranged in panels. The classified result map layer categories
is arranged along the vertical axis of the table, while the reference map layer categories
along the horizontal axis. Each panel has a maximum of 5 categories (9 if wide format)
across the top. In addition, the last column of the last panel reflects a cross total of
each column for each row. All of the categories of the map layer arranged along the
vertical axis, i.e., the reference map layer, are included in each panel. There is a
total at the bottom of each column representing the sum of all the rows in that column.
OUTPUT VARIABLES
All output variables (except kappa variance) have been validated to produce correct values
in accordance to formulas given by Rossiter, D.G., 2004. "Technical Note: Statistical
methods for accuracy assessment of classified thematic maps".
Observations
Overall count of observed cells (sum of both correct and incorrect ones).
Correct
Overall count of correct cells (cells with equal value in reference and classification
maps).
Overall accuracy
Number of correct cells divided by overall cell count (expressed in percent).
User’s accuracy
Share of correctly classified cells out of all cells classified as belonging to
specified class (expressed in percent). Inverse of commission error.
Commission
Commission error = 100 - user’s accuracy.
Producer’s accuracy
Share of correctly classified cells out of all cells known to belong to specified
class (expressed in percent). Inverse of omission error.
Omission
Omission error = 100 - producer’s accuracy.
Kappa
Choen’s kappa index value.
Kappa variance
Variance of kappa index. Correctness needs to be validated.
Conditional kappa
Conditional user’s kappa for specified class.
MCC
Matthews (Mattheus) Correlation Coefficient is implemented according to Grandini, M.,
Bagli, E., Visani, G. 2020. "Metrics for multi-class classification: An overview."
NOTES
It is recommended to reclassify categories of classified result map layer into a more
manageable number before running r.kappa on the classified raster map layer. Because
r.kappa calculates and then reports information for each and every category.
NA’s in output mean it was not possible to calculate the value (e.g. calculation would
involve division by zero). In JSON output NA’s are represented with value null. If there
is no overlap between both maps, a warning is printed and output values are set to 0 or
null respectively.
The Estimated kappa value in r.kappa is the value only for one class, i.e. the observed
agreement between the classifications for those observations that have been classified by
classifier 1 into the class i. In other words, here the choice of reference is important.
It is calculated as:
kpp[i] = (pii[i] - pi[i] * pj[i]) / (pi[i] - pi[i] * pj[i]);
where=
• pii[i] is the probability of agreement (i.e. number of pixels for which there is
agreement divided by total number of assessed pixels)
• Pi[i] is the probability of classification i having classified the point as i
• Pj[i] is the probability of classification j having classified the point as i.
Some of reported values (overall accuracy, Choen’s kappa, MCC) can be misleading if cell
count among classes is not balanced. See e.g. Powers, D.M.W., 2012. "The Problem with
Kappa"; Zhu, Q., 2020. "On the performance of Matthews correlation coefficient (MCC) for
imbalanced dataset".
EXAMPLE
Example for North Carolina sample dataset:
g.region raster=landclass96 -p
r.kappa -w classification=landuse96_28m reference=landclass96
# export Kappa matrix as CSV file "kappa.csv"
r.kappa classification=landuse96_28m reference=landclass96 output=kappa.csv -m -h
Verification of classified LANDSAT scene against training areas:
r.kappa -w classification=lsat7_2002_classes reference=training
SEE ALSO
g.region, r.category, r.mask, r.reclass, r.report, r.stats
AUTHORS
Tao Wen, University of Illinois at Urbana-Champaign, Illinois
Maris Nartiss, University of Latvia (JSON output, MCC)
SOURCE CODE
Available at: r.kappa source code (history)
Accessed: Thursday Aug 01 11:30:03 2024
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