Classify time series or data cubes

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 column predicted.

• 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 using sits_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 sits_train

...

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:

1. sits_cube: selects a ARD image collection from a cloud provider.

2. sits_cube_copy: copies an ARD image collection from a cloud provider to a local directory for faster processing.

3. sits_regularize: create a regular data cube from an ARD image collection.

4. sits_apply: create new indices by combining bands of a regular data cube (optional).

5. sits_get_data: extract time series from a regular data cube based on user-provided labelled samples.

6. sits_train: train a machine learning model based on image time series.

7. sits_classify: classify a data cube using a machine learning model and obtain a probability cube.

8. sits_smooth: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.

9. 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;

• light gradient boosting: sits_lightgbm;

• multi-layer perceptrons: sits_mlp;

• temporal CNN: sits_tempcnn;

• residual network encoders: sits_resnet;

• LSTM with convolutional networks: sits_lstm_fcn;

• temporal self-attention encoders: sits_lighttae and sits_tae.

Please refer to the sits documentation available in https://e-sensing.github.io/sitsbook/ for detailed examples.

Author

Rolf Simoes, rolfsimoes@gmail.com

Gilberto Camara, gilberto.camara@inpe.br

Felipe Carvalho, lipecaso@gmail.com

Felipe Carlos, efelipecarlos@gmail.com