This function calculates the accuracy of the classification
result. The input is either a set of classified time series or a classified
data cube. Classified time series are produced
by sits_classify.
Classified images are generated using sits_classify
followed by sits_label_classification.
For a set of time series, sits_accuracy creates a confusion matrix and
calculates the resulting statistics using package caret. For a
classified image, the function uses an area-weighted technique
proposed by Olofsson et al. according to references [1-3] to produce reliable
accuracy estimates at 95% confidence level. 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 class 'sits'
sits_accuracy(data, ...)
# S3 method for class 'class_vector_cube'
sits_accuracy(data, ..., prediction_attr, reference_attr)
# S3 method for class 'class_cube'
sits_accuracy(data, ..., validation, method = "olofsson")
# S3 method for class 'raster_cube'
sits_accuracy(data, ...)
# S3 method for class 'derived_cube'
sits_accuracy(data, ...)
# S3 method for class 'tbl_df'
sits_accuracy(data, ...)
# Default S3 method
sits_accuracy(data, ...)Arguments
- data
Either a data cube with classified images or a set of time series
- ...
Specific parameters
- prediction_attr
Name of the column of the segments object that contains the predicted values (only for vector class cubes)
- reference_attr
Name of the column of the segments object that contains the reference values (only for vector class cubes)
- validation
Samples for validation (see below) Only required when data is a raster class cube.
- method
A character with 'olofsson' or 'pixel' to compute accuracy (only for raster class cubes)
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. The result is assigned to class "sits_accuracy" and can be visualized directly on the screen.
Note
The `validation` data needs to contain the following columns: "latitude", "longitude", "start_date", "end_date", and "label". It can be either a path to a CSV file, a sits tibble, a data frame, or an sf object.
When `validation` is an sf object, the columns "latitude" and "longitude" are not required as the locations are extracted from the geometry column. The `centroid` is calculated before extracting the location values for any geometry type.
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_ndvi, frac = 0.5)
test_data <- sits_sample(samples_modis_ndvi, frac = 0.5)
rfor_model <- sits_train(train_data, sits_rfor())
points_class <- sits_classify(
data = test_data, ml_model = rfor_model
)
acc <- sits_accuracy(points_class)
# show accuracy for a data cube classification
# create a random forest model
rfor_model <- sits_train(samples_modis_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 = "MOD13Q1-6.1",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# label the probability cube
label_cube <- sits_label_classification(
probs_cube,
output_dir = tempdir()
)
# 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 = ground_truth)
}