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.

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:

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


[Package sits version 1.5.3 Index]