Train classification models — sits

Given a tibble with a set of time series, returns trained models. Currently, sits supports the following models:

support vector machines: sits_svm;
random forests: sits_rfor;
extreme gradient boosting: sits_xgboost;
light gradient boosting: sits_lightgbm;
multi-layer perceptrons: sits_mlp;
temporal CNN: sits_tempcnn;
residual network encoders: sits_resnet;
LSTM with convolutional networks: sits_lstm_fcn;
temporal self-attention encoders: sits_lighttae and sits_tae.

Usage

sits_train(samples, ml_method = sits_svm())

Arguments

samples: Time series with the training samples.
ml_method: Machine learning method.

Value

Model fitted to input data to be passed to sits_classify

Note

The main sits classification workflow has the following steps:

sits_cube: selects a ARD image collection from a cloud provider.
sits_cube_copy: copies an ARD image collection from a cloud provider to a local directory for faster processing.
sits_regularize: create a regular data cube from an ARD image collection.
sits_apply: create new indices by combining bands of a regular data cube (optional).
sits_get_data: extract time series from a regular data cube based on user-provided labelled samples.
sits_train: train a machine learning model based on image time series.
sits_classify: classify a data cube using a machine learning model and obtain a probability cube.
sits_smooth: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.
sits_label_classification: produce a classified map by selecting the label with the highest probability from a smoothed cube.

sits_train provides a standard interface to machine learning models. It takes two mandatory parameters: the training data (samples) and the ML algorithm (ml_method). The output is a model that can be used to classify individual time series or data cubes with sits_classify.

sits provides a set of default values for all classification models. These settings have been chosen based on testing by the authors. Nevertheless, users can control all parameters for each model. Novice users can rely on the default values, while experienced ones can fine-tune deep learning models using sits_tuning.

Author

Rolf Simoes, rolfsimoes@gmail.com

Gilberto Camara, gilberto.camara@inpe.br

Alexandre Ywata de Carvalho, alexandre.ywata@ipea.gov.br

Examples

if (sits_run_examples()) {
    # Retrieve the set of samples for Mato Grosso
    # fit a training model (rfor model)
    ml_model <- sits_train(samples_modis_ndvi, sits_rfor(num_trees = 50))
    # get a point and classify the point with the ml_model
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    class <- sits_classify(
        data = point_ndvi, ml_model = ml_model
    )
}