sits (version 1.5.3)

sits_tae: Train a model using Temporal Self-Attention Encoder

Description

Implementation of Temporal Attention Encoder (TAE) for satellite image time series classification.

Usage

sits_tae(
  samples = NULL,
  samples_validation = NULL,
  epochs = 150L,
  batch_size = 64L,
  validation_split = 0.2,
  optimizer = torch::optim_adamw,
  opt_hparams = list(lr = 0.001, eps = 1e-08, weight_decay = 1e-06),
  lr_decay_epochs = 1L,
  lr_decay_rate = 0.95,
  patience = 20L,
  min_delta = 0.01,
  verbose = FALSE
)

Value

A fitted model to be used for classification.

Arguments

samples

Time series with the training samples.

samples_validation

Time series with the validation samples. if the samples_validation parameter is provided, the validation_split parameter is ignored.

epochs

Number of iterations to train the model.

batch_size

Number of samples per gradient update.

validation_split

Number between 0 and 1. Fraction of training data to be used as validation data.

optimizer

Optimizer function to be used.

opt_hparams

Hyperparameters for optimizer: lr : Learning rate of the optimizer eps: Term added to the denominator to improve numerical stability. weight_decay: L2 regularization

lr_decay_epochs

Number of epochs to reduce learning rate.

lr_decay_rate

Decay factor for reducing learning rate.

patience

Number of epochs without improvements until training stops.

min_delta

Minimum improvement to reset the patience counter.

verbose

Verbosity mode (TRUE/FALSE). Default is FALSE.

Author

Charlotte Pelletier,

Gilberto Camara,

Rolf Simoes,

Felipe Souza,

References

Vivien Garnot, Loic Landrieu, Sebastien Giordano, and Nesrine Chehata, "Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention", 2020 Conference on Computer Vision and Pattern Recognition. pages 12322-12331. DOI: 10.1109/CVPR42600.2020.01234

Schneider, Maja; Körner, Marco, "[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention." ReScience C 7 (2), 2021. DOI: 10.5281/zenodo.4835356

Examples

if (sits_run_examples()) {
    # create a TAE model
    torch_model <- sits_train(samples_modis_ndvi, sits_tae())
    # plot the model
    plot(torch_model)
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = torch_model, output_dir = tempdir()
    )
    # plot the probability cube
    plot(probs_cube)
    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
    # plot the smoothed cube
    plot(bayes_cube)
    # label the probability cube
    label_cube <- sits_label_classification(
        bayes_cube,
        output_dir = tempdir()
    )
    # plot the labelled cube
    plot(label_cube)
}