validateMap: Map accuracy assessment

In RStoolbox: Remote Sensing Data Analysis

Map accuracy assessment

Description

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")

## 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)

RStoolbox documentation built on May 29, 2024, 6:34 a.m.