sits (version 1.5.3)

sits_lighttae: Train a model using Lightweight Temporal Self-Attention Encoder

Description

Implementation of Light Temporal Attention Encoder (L-TAE) for satellite image time series

Usage

sits_lighttae(
  samples = NULL,
  samples_validation = NULL,
  epochs = 150L,
  batch_size = 128L,
  validation_split = 0.2,
  optimizer = torch::optim_adamw,
  opt_hparams = list(lr = 5e-04, eps = 1e-08, weight_decay = 7e-04),
  lr_decay_epochs = 50L,
  lr_decay_rate = 1,
  patience = 20L,
  min_delta = 0.01,
  verbose = FALSE
)

Value

A fitted model to be used for classification of data cubes.

Arguments

samples

Time series with the training samples (tibble of class "sits").

samples_validation

Time series with the validation samples (tibble of class "sits"). If samples_validation parameter is provided, validation_split is ignored.

epochs

Number of iterations to train the model (integer, min = 1, max = 20000).

batch_size

Number of samples per gradient update (integer, min = 16L, max = 2048L)

validation_split

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 rate.

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 in loss function to reset the patience counter.

verbose

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

Author

Gilberto Camara,

Rolf Simoes,

Charlotte Pelletier,

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

Vivien Garnot, Loic Landrieu, "Lightweight Temporal Self-Attention for Classifying Satellite Images Time Series", arXiv preprint arXiv:2007.00586, 2020.

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 lightTAE model
    torch_model <- sits_train(samples_modis_ndvi, sits_lighttae())
    # 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)
}