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sits

Source: R/sits-package.R
sits-package.Rd

Satellite Image Time Series Analysis for Earth Observation Data Cubes

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.

Purpose

The SITS package provides a set of tools for analysis, visualization and classification of satellite image time series. It includes methods for filtering, clustering, classification, and post-processing.

See also

Useful links:

Author

Maintainer: Gilberto Camara gilberto.camara.inpe@gmail.com [thesis advisor]

Authors:

Other contributors: