- img
SpatRaster. Typically remote sensing imagery, which is to be classified.
- trainData
sf or sp spatial vector data containing the training locations (POINTs,or POLYGONs).
- valData
Ssf or sp spatial vector data containing the validation locations (POINTs,or POLYGONs) (optional).
- responseCol
Character or integer giving the column in trainData
, which contains the response variable. Can be omitted, when trainData
has only one column.
- nSamples
Integer. Number of samples per land cover class. If NULL
all pixels covered by training polygons are used (memory intensive!). Ignored if trainData consists of POINTs.
- nSamplesV
Integer. Number of validation samples per land cover class. If NULL
all pixels covered by validation polygons are used (memory intensive!). Ignored if valData consists of POINTs.
- polygonBasedCV
Logical. If TRUE
model tuning during cross-validation is conducted on a per-polygon basis. Use this to deal with overfitting issues. Does not affect training data supplied as SpatialPointsDataFrames.
- trainPartition
Numeric. Partition (polygon based) of trainData
that goes into the training data set between zero and one. Ignored if valData
is provided.
- model
Character. Which model to use. See train for options. Defaults to randomForest ('rf'). In addition to the standard caret models, a maximum likelihood classification is available via model = 'mlc'
.
- tuneLength
Integer. Number of levels for each tuning parameter (see train for details).
- kfold
Integer. Number of cross-validation resamples during model tuning.
- sampling
Character. Describes the type of additional sampling that is conducted after resampling (usually to resolve class imbalances), from caret. Currently supported are up
, down
, smote
, and rose
. Note, that smote
requires the packages themis
and rose
the package ROSE
. Latter is noly for binary classification problems.
- minDist
Numeric. Minumum distance between training and validation data,
e.g. minDist=1
clips validation polygons to ensure a minimal distance of one pixel (pixel size according to img
) to the next training polygon.
Requires all data to carry valid projection information.
- mode
Character. Model type: 'regression' or 'classification'.
- predict
Logical. Produce a map (TRUE, default) or only fit and validate the model (FALSE).
- predType
Character. Type of the final output raster. Either "raw" for class predictions or "prob" for class probabilities. Class probabilities are not available for all classification models (predict.train).
- filename
Path to output file (optional). If NULL
, standard raster handling will apply, i.e. storage either in memory or in the raster temp directory.
- verbose
Logical. prints progress and statistics during execution
- overwrite
logical. Overwrite spatial prediction raster if it already exists.
- ...
further arguments to be passed to train