sits (version 1.5.0)

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: (a) support vector machines: sits_svm; (b) random forests: sits_rfor; (c) extreme gradient boosting: sits_xgboost; (d) multi-layer perceptrons: sits_mlp; (e) 1D CNN: sits_tempcnn; (f) deep residual networks: sits_resnet; (g) self-attention encoders: sits_lighttae.

Usage

sits_classify(
  data,
  ml_model,
  ...,
  filter_fn = NULL,
  multicores = 2L,
  progress = TRUE
)
# S3 method for sits
sits_classify(
  data,
  ml_model,
  ...,
  filter_fn = NULL,
  impute_fn = impute_linear(),
  multicores = 2L,
  gpu_memory = 16,
  progress = TRUE
)
# S3 method for raster_cube
sits_classify(
  data,
  ml_model,
  ...,
  roi = NULL,
  filter_fn = NULL,
  impute_fn = impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 8L,
  multicores = 2L,
  gpu_memory = 16,
  output_dir,
  version = "v1",
  verbose = FALSE,
  progress = TRUE
)
# S3 method for derived_cube
sits_classify(data, ml_model, ...)
# S3 method for tbl_df
sits_classify(data, ml_model, ...)
# S3 method for segs_cube
sits_classify(
  data,
  ml_model,
  ...,
  roi = NULL,
  filter_fn = NULL,
  impute_fn = impute_linear(),
  start_date = NULL,
  end_date = NULL,
  memsize = 8L,
  multicores = 2L,
  gpu_memory = 16,
  output_dir,
  version = "v1",
  n_sam_pol = NULL,
  verbose = FALSE,
  progress = TRUE
)
# S3 method for default
sits_classify(data, ml_model, ...)

Value

Time series with predicted labels for each point (tibble of class "sits") or a data cube with probabilities for each class (tibble of class "probs_cube").

Arguments

data

Data cube (tibble of class "raster_cube")

ml_model

R model trained by sits_train (closure of class "sits_model")

...

Other parameters for specific functions.

filter_fn

Smoothing filter to be applied - optional (clousure containing object of class "function").

multicores

Number of cores to be used for classification (integer, min = 1, max = 2048).

progress

Logical: Show progress bar?

impute_fn

Imputation function to remove NA.

gpu_memory

Memory available in GPU in GB (default = 16)

roi

Region of interest (either an sf object, shapefile, or a numeric vector with named XY values ("xmin", "xmax", "ymin", "ymax") or named lat/long values ("lon_min", "lat_min", "lon_max", "lat_max").

start_date

Start date for the classification (Date in YYYY-MM-DD format).

end_date

End date for the classification (Date im YYYY-MM-DD format).

memsize

Memory available for classification in GB (integer, min = 1, max = 16384).

output_dir

Valid directory for output file. (character vector of length 1).

version

Version of the output (character vector of length 1).

verbose

Logical: print information about processing time?

n_sam_pol

Number of time series per segment to be classified (integer, min = 10, max = 50).

Author

Rolf Simoes,

Gilberto Camara,

Examples

if (sits_run_examples()) {
    # Example of classification of a time series
    # Retrieve the samples for Mato Grosso
    # train a random forest model
    rf_model <- sits_train(samples_modis_ndvi, ml_method = sits_rfor)

    # classify the point
    point_ndvi <- sits_select(point_mt_6bands, bands = c("NDVI"))
    point_class <- sits_classify(
        data = point_ndvi, ml_model = 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
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube,
        ml_model = rf_model,
        output_dir = tempdir(),
        version = "ex_classify"
    )
    # label the probability cube
    label_cube <- sits_label_classification(
        probs_cube,
        output_dir = tempdir(),
        version = "ex_classify"
    )
    # plot the classified image
    plot(label_cube)
    # segmentation
    # segment the image
    segments <- sits_segment(
        cube = cube,
        seg_fn = sits_slic(step = 5,
                       compactness = 1,
                       dist_fun = "euclidean",
                       avg_fun = "median",
                       iter = 50,
                       minarea = 10,
                       verbose = FALSE
                       ),
        output_dir = tempdir()
    )
    # Create a classified vector cube
    probs_segs <- sits_classify(
        data = segments,
        ml_model = rf_model,
        output_dir = tempdir(),
        multicores = 4,
        version = "segs"
    )
    # Create a labelled vector cube
    class_segs <- sits_label_classification(
        cube = probs_segs,
        output_dir = tempdir(),
        multicores = 2,
        memsize = 4,
        version = "segs_classify"
    )
    # plot class_segs
    plot(class_segs)
}