sits_classify {sits} | R Documentation |
Classify time series or data cubes
Description
This function classifies a set of time series or data cube using
a trained model prediction model created by sits_train
.
The sits_classify
function takes three types of data as input
and produce there types of output. Users should call
sits_classify
but be aware that the parameters
are different for each type of input.
sits_classify.sits
is called when the input is a set of time series. The output is the same set with the additional columnpredicted
.sits_classify.raster_cube
is called when the input is a regular raster data cube. The output is a probability cube, which has the same tiles as the raster cube. Each tile contains a multiband image; each band contains the probability that each pixel belongs to a given class. Probability cubes are objects of class "probs_cube".sits_classify.vector_cube
is called for vector data cubes. Vector data cubes are produced when closed regions are obtained from raster data cubes usingsits_segment
. Classification of a vector data cube produces a vector data structure with additional columns expressing the class probabilities for each object. Probability cubes for vector data cubes are objects of class "probs_vector_cube".
Usage
sits_classify(data, ml_model, ...)
## S3 method for class 'tbl_df'
sits_classify(data, ml_model, ...)
## S3 method for class 'derived_cube'
sits_classify(data, ml_model, ...)
## Default S3 method:
sits_classify(data, ml_model, ...)
Arguments
data |
Data cube (tibble of class "raster_cube") |
ml_model |
R model trained by |
... |
Other parameters for specific functions. |
Value
Time series with predicted labels for each point (tibble of class "sits") or a data cube with probabilities for each class (tibble of class "probs_cube").
Note
The main sits
classification workflow has the following steps:
sits_cube
: selects a ARD image collection from a cloud provider.sits_cube_copy
: copies an ARD image collection from a cloud provider to a local directory for faster processing.sits_regularize
: create a regular data cube from an ARD image collection.sits_apply
: create new indices by combining bands of a regular data cube (optional).sits_get_data
: extract time series from a regular data cube based on user-provided labelled samples.sits_train
: train a machine learning model based on image time series.sits_classify
: classify a data cube using a machine learning model and obtain a probability cube.sits_smooth
: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.sits_label_classification
: produce a classified map by selecting the label with the highest probability from a smoothed cube.
SITS supports the following models:
support vector machines:
sits_svm
;random forests:
sits_rfor
;extreme gradient boosting:
sits_xgboost
;multi-layer perceptrons:
sits_mlp
;temporal CNN:
sits_tempcnn
;temporal self-attention encoders:
sits_lighttae
andsits_tae
.
Please refer to the sits documentation available in <https://e-sensing.github.io/sitsbook/> for detailed examples.
Author(s)
Rolf Simoes, rolfsimoes@gmail.com
Gilberto Camara, gilberto.camara@inpe.br
Felipe Carvalho, lipecaso@gmail.com
Felipe Carlos, efelipecarlos@gmail.com