sits (version 1.1.0)

sits_accuracy: Assess classification accuracy (area-weighted method)

Description

This function calculates the accuracy of the classification result. For a set of time series, it creates a confusion matrix and then calculates the resulting statistics using the R package "caret". The time series needs to be classified using sits_classify.

Classified images are generated using sits_classify followed by sits_label_classification. For a classified image, the function uses an area-weighted technique proposed by Olofsson et al. according to [1-3] to produce more reliable accuracy estimates at 95

In both cases, it provides an accuracy assessment of the classified, including Overall Accuracy, Kappa, User's Accuracy, Producer's Accuracy and error matrix (confusion matrix)

Usage

sits_accuracy(data, ...)
# S3 method for sits
sits_accuracy(data, ...)
# S3 method for classified_image
sits_accuracy(data, ..., validation_csv)

Value

A list of lists: The error_matrix, the class_areas, the unbiased estimated areas, the standard error areas, confidence interval 95

and the accuracy (user, producer, and overall), or NULL if the data is empty. A confusion matrix assessment produced by the caret package.

Arguments

data

Either a data cube with classified images or a set of time series

...

Specific parameters

validation_csv

A CSV file path with validation data

Author

Rolf Simoes,

Alber Sanchez,

References

[1] Olofsson, P., Foody, G.M., Stehman, S.V., Woodcock, C.E. (2013). Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing of Environment, 129, pp.122-131.

[2] Olofsson, P., Foody G.M., Herold M., Stehman, S.V., Woodcock, C.E., Wulder, M.A. (2014) Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, pp. 42-57.

[3] FAO, Map Accuracy Assessment and Area Estimation: A Practical Guide. National forest monitoring assessment working paper No.46/E, 2016.

Examples

if (sits_run_examples()) {
    # show accuracy for a set of samples
    train_data <- sits_sample(samples_modis_4bands, n = 200)
    test_data <- sits_sample(samples_modis_4bands, n = 200)
    rfor_model <- sits_train(train_data, sits_rfor())
    points_class <- sits_classify(test_data, rfor_model)
    acc <- sits_accuracy(points_class)

    # show accuracy for a data cube classification
    # select a set of samples
    samples_ndvi <- sits_select(samples_modis_4bands, bands = c("NDVI"))
    # create a random forest model
    rfor_model <- sits_train(samples_ndvi, sits_rfor())
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir,
        delim = "_",
        parse_info = c("X1", "X2", "tile", "band", "date")
    )
    # classify a data cube
    probs_cube <- sits_classify(data = cube, ml_model = rfor_model)
    # label the probability cube
    label_cube <- sits_label_classification(probs_cube)
    # obtain the ground truth for accuracy assessment
    ground_truth <- system.file("extdata/samples/samples_sinop_crop.csv",
        package = "sits"
    )
    # make accuracy assessment
    as <- sits_accuracy(label_cube, validation_csv = ground_truth)
}