validate a map from a classification or regression model. This can be useful to update the accuracy assessment after filtering, e.g. for a minimum mapping unit.
Usage
validateMap(
map,
valData,
responseCol,
nSamplesV = 500,
mode = "classification",
classMapping = NULL
)Arguments
- map
SpatRaster. The classified map.
- valData
sf object with validation data (POLYGONs or POINTs).
- responseCol
Character. Column containing the validation data in attribute table of
valData.- nSamplesV
Integer. Number of pixels to sample for validation (only applies to polygons).
- mode
Character. Either 'classification' or 'regression'.
- classMapping
optional data.frame with columns
'class'and'classID'defining the mapping from raster integers to class names.
Value
Returns a structured list includng the preformance and confusion-matrix of your then validated input data
Examples
library(caret)
library(terra)
## Training data
poly <- readRDS(system.file("external/trainingPolygons_lsat.rds", package="RStoolbox"))
## Split training data in training and validation set (50%-50%)
splitIn <- createDataPartition(poly$class, p = .5)[[1]]
train <- poly[splitIn,]
val <- poly[-splitIn,]
## Classify (deliberately poorly)
sc <- superClass(lsat, trainData = train, responseCol = "class", nSamples = 50, model = "mlc")
#> 17:03:42 | Begin sampling training data
#> 17:03:42 | Starting to fit model
#> 17:03:43 | Starting spatial predict
#> ******************** Model summary ********************
#> Maximum Likelihood Classification
#>
#> 274 samples
#> 7 predictor
#> 4 classes: 'cleared', 'fallen_dry', 'forest', 'water'
#>
#> No pre-processing
#> Resampling: Cross-Validated (5 fold)
#> Summary of sample sizes: 219, 219, 219, 220, 219
#> Resampling results:
#>
#> Accuracy Kappa
#> 1 1
#>
#> [[1]]
#> TrainAccuracy TrainKappa method
#> 1 1 1 custom
#>
#> [[2]]
#> Cross-Validated (5 fold) Confusion Matrix
#>
#> (entries are average cell counts across resamples)
#>
#> Reference
#> Prediction cleared fallen_dry forest water
#> cleared 16.0 0.0 0.0 0.0
#> fallen_dry 0.0 6.8 0.0 0.0
#> forest 0.0 0.0 16.0 0.0
#> water 0.0 0.0 0.0 16.0
#>
#> Accuracy (average) : 1
#>
#>
#> ******************** Validation summary ********************
#> [1] "No independent validation was performed!"
## Polish map with majority filter
polishedMap <- focal(sc$map, matrix(1,3,3), fun = modal)
## Validation
## Before filtering
val0 <- validateMap(sc$map, valData = val, responseCol = "class",
classMapping = sc$classMapping)
## After filtering
val1 <- validateMap(polishedMap, valData = val, responseCol = "class",
classMapping = sc$classMapping)