sits_train: Train classification models

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Train classification models

Description

Given a tibble with a set of distance measures, returns trained models. Currently, sits supports the following models: 'svm' (see sits_svm), random forests (see sits_rfor), extreme gradient boosting (see sits_xgboost), and different deep learning functions, including multi-layer perceptrons (see sits_mlp), 1D convolution neural networks sits_tempcnn, deep residual networks sits_resnet and self-attention encoders sits_lighttae

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

Author(s)

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Alexandre Ywata de Carvalho, [e-mail]

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
    )
}

sits documentation built on Nov. 2, 2023, 5:59 p.m.