sits (version 1.1.0)

sits_classify: Classify time series or data cubes

Description

This function classifies a set of time series or data cube given a trained model prediction model created by sits_train.

SITS supports the following models:

• support vector machines: see sits_svm

• random forests: see sits_rfor

• extreme gradient boosting: see sits_xgboost

• multi-layer perceptrons: see sits_mlp

• 1D CNN: see sits_tempcnn

• deep residual networks:see sits_resnet

• self-attention encoders:see sits_lighttae

Usage

sits_classify(data, ml_model, ...)
# S3 method for sits
sits_classify(data, ml_model, ..., filter_fn = NULL, multicores = 2)
# S3 method for raster_cube
sits_classify(
  data,
  ml_model,
  ...,
  roi = NULL,
  filter_fn = NULL,
  impute_fn = sits_impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 8,
  multicores = 2,
  output_dir = ".",
  version = "v1",
  verbose = FALSE,
  progress = FALSE
)

Value

Predicted data (classified time series) or a data cube with probabilities for each class.

Arguments

data

Data cube.

ml_model

R model trained by sits_train.

...

Other parameters for specific functions.

filter_fn

Smoothing filter to be applied (if desired).

multicores

Number of cores to be used for classification.

roi

Region of interest (see below)

impute_fn

Impute function to replace NA.

start_date

Start date for the classification.

end_date

End date for the classification.

memsize

Memory available for classification (in GB).

output_dir

Directory for output file.

version

Version of the output (for multiple classifications).

verbose

Print information about processing time?

progress

Show progress bar?

Author

Rolf Simoes,

Gilberto Camara,

Examples

if (sits_run_examples()) {
    # Example of classification of a time series
    # Retrieve the samples for Mato Grosso
    # select the NDVI band
    samples_ndvi <- sits_select(samples_modis_4bands, bands = c("NDVI"))
    # train a random forest model
    rf_model <- sits_train(samples_ndvi, ml_method = sits_rfor)

    # classify the point
    point_ndvi <- sits_select(point_mt_6bands, bands = c("NDVI"))
    point_class <- sits_classify(point_ndvi, rf_model)
    plot(point_class)

    # Example of classification of a data cube
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir,
        delim = "_",
        parse_info = c("X1", "X2", "tile", "band", "date")
    )
    # classify a data cube
    probs_cube <- sits_classify(data = cube, ml_model = rf_model)
    # label the probability cube
    label_cube <- sits_label_classification(probs_cube)
    # plot the classified image
    plot(label_cube)
}