sits_svm: Train support vector machine models

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

Train support vector machine models

Description

This function receives a tibble with a set of attributes X for each observation Y. These attributes are the values of the time series for each band. The SVM algorithm is used for multiclass-classification. For this purpose, it uses the "one-against-one" approach, in which k(k-1)/2 binary classifiers are trained; the appropriate class is found by a voting scheme. This function is a front-end to the "svm" method in the "e1071" package. Please refer to the documentation in that package for more details.

Usage

sits_svm(
  samples = NULL,
  formula = sits_formula_linear(),
  scale = FALSE,
  cachesize = 1000,
  kernel = "radial",
  degree = 3,
  coef0 = 0,
  cost = 10,
  tolerance = 0.001,
  epsilon = 0.1,
  cross = 10,
  ...
)

Arguments

samples	Time series with the training samples.
formula	Symbolic description of the model to be fit. (default: sits_formula_linear).
scale	Logical vector indicating the variables to be scaled.
cachesize	Cache memory in MB (default = 1000).
kernel	Kernel used in training and predicting. options: "linear", "polynomial", "radial", "sigmoid" (default: "radial").
degree	Exponential of polynomial type kernel (default: 3).
coef0	Parameter needed for kernels of type polynomial and sigmoid (default: 0).
cost	Cost of constraints violation (default: 10).
tolerance	Tolerance of termination criterion (default: 0.001).
epsilon	Epsilon in the insensitive-loss function (default: 0.1).
cross	Number of cross validation folds applied to assess the quality of the model (default: 10).
...	Other parameters to be passed to e1071::svm function.

Value

Model fitted to input data (to be passed to sits_classify)

Note

Please refer to the sits documentation available in <https://e-sensing.github.io/sitsbook/> for detailed examples.

Author(s)

Alexandre Ywata de Carvalho, [e-mail]

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Examples

if (sits_run_examples()) {
    # Example of training a model for time series classification
    # Retrieve the samples for Mato Grosso
    # train an SVM model
    ml_model <- sits_train(samples_modis_ndvi, ml_method = sits_svm)
    # classify the point
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    # classify the point
    point_class <- sits_classify(
        data = point_ndvi, ml_model = ml_model
    )
    plot(point_class)
}

sits documentation built on Sept. 11, 2024, 6:36 p.m.