sits_validate: Validate time series samples

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Validate time series samples

Description

One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set).

The function takes two arguments: a set of time series with a machine learning model and another set with validation samples. If the validation sample set is not provided, The sample dataset is split into two parts, as defined by the parameter validation_split. The accuracy is determined by the result of the validation test set.

This function returns the confusion matrix, and Kappa values.

Usage

sits_validate(
  samples,
  samples_validation = NULL,
  validation_split = 0.2,
  ml_method = sits_rfor(),
  gpu_memory = 4,
  batch_size = 2^gpu_memory
)

Arguments

samples	Time series to be validated (class "sits").
samples_validation	Optional: Time series used for validation (class "sits")
validation_split	Percent of original time series set to be used for validation if samples_validation is NULL (numeric value).
ml_method	Machine learning method (function)
gpu_memory	Memory available in GPU in GB (default = 4)
batch_size	Batch size for GPU classification.

Value

A caret::confusionMatrix object to be used for validation assessment.

Note

#' When using a GPU for deep learning, gpu_memory indicates the memory of the graphics card which is available for processing. The parameter batch_size defines the size of the matrix (measured in number of rows) which is sent to the GPU for classification. Users can test different values of batch_size to find out which one best fits their GPU architecture.

It is not possible to have an exact idea of the size of Deep Learning models in GPU memory, as the complexity of the model and factors such as CUDA Context increase the size of the model in memory. Therefore, we recommend that you leave at least 1GB free on the video card to store the Deep Learning model that will be used.

For users of Apple M3 chips or similar with a Neural Engine, be aware that these chips share memory between the GPU and the CPU. Tests indicate that the memsize should be set to half to the total memory and the batch_size parameter should be a small number (we suggest the value of 64). Be aware that increasing these parameters may lead to memory conflicts.

Author(s)

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Examples

if (sits_run_examples()) {
    samples <- sits_sample(cerrado_2classes, frac = 0.5)
    samples_validation <- sits_sample(cerrado_2classes, frac = 0.5)
    conf_matrix_1 <- sits_validate(
         samples = samples,
         samples_validation = samples_validation,
         ml_method = sits_rfor()
   )
   conf_matrix_2 <- sits_validate(
         samples = cerrado_2classes,
         validation_split = 0.2,
         ml_method = sits_rfor()
   )
}

e-sensing/sits documentation built on Feb. 13, 2025, 2:22 a.m.