# Satellite Image Time Series Analysis for Earth Observation Data Cubes

## Documentation for package ‘sits’ version 1.3.0

- DESCRIPTION file.
- Code demos. Use demo() to run them.

## Help Pages

C P S misc

sits-package | sits

## -- C --

cerrado_2classes | Samples of classes Cerrado and Pasture

## -- P --

plot | Plot time series
plot.class_cube | Plot classified images
plot.geo_distances | Make a kernel density plot of samples distances.
plot.patterns | Plot patterns that describe classes
plot.predicted | Plot time series predictions
plot.probs_cube | Plot probability cubes
plot.raster_cube | Plot RGB data cubes
plot.rfor_model | Plot Random Forest model
plot.sits | Plot time series
plot.sits_accuracy | Plot confusion matrix
plot.som_evaluate_cluster | Plot confusion between clusters
plot.som_map | Plot a SOM map
plot.torch_model | Plot Torch (deep learning) model
plot.uncertainty_cube | Plot uncertainty cubes
plot.variance_cube | Plot variance cubes
plot.xgb_model | Plot XGB model
point_mt_6bands | A time series sample with data from 2000 to 2016

## -- S --

samples_l8_rondonia_2bands | Samples of Amazon tropical forest biome for deforestation analysis
samples_modis_ndvi | Samples of nine classes for the state of Mato Grosso
sits | sits
sits_accuracy | Assess classification accuracy (area-weighted method)
sits_accuracy.class_cube | Assess classification accuracy (area-weighted method)
sits_accuracy.sits | Assess classification accuracy (area-weighted method)
sits_apply | Apply a function on a set of time series
sits_apply.raster_cube | Apply a function on a set of time series
sits_apply.sits | Apply a function on a set of time series
sits_as_sf | Return a sits_tibble or raster_cube as an sf object.
sits_as_sf.raster_cube | Return a sits_tibble or raster_cube as an sf object.
sits_as_sf.sits | Return a sits_tibble or raster_cube as an sf object.
sits_bands | Get the names of the bands
sits_bands.patterns | Get the names of the bands
sits_bands.raster_cube | Get the names of the bands
sits_bands.sits | Get the names of the bands
sits_bands.sits_model | Get the names of the bands
sits_bbox | Get the bounding box of the data
sits_bbox.raster_cube | Get the bounding box of the data
sits_bbox.sits | Get the bounding box of the data
sits_classify | Classify time series or data cubes
sits_classify.raster_cube | Classify time series or data cubes
sits_classify.sits | Classify time series or data cubes
sits_clustering | Find clusters in time series samples
sits_cluster_clean | Removes labels that are minority in each cluster.
sits_cluster_dendro | Find clusters in time series samples
sits_cluster_frequency | Show label frequency in each cluster produced by dendrogram analysis
sits_colors | Function to retrieve sits color table
sits_colors_reset | Function to reset sits color table
sits_colors_set | Function to set sits color table
sits_colors_show | Function to show colors in SITS
sits_color_value | Function to retrieve sits color value
sits_combine_predictions | Estimate ensemble prediction based on list of probs cubes
sits_combine_predictions.average | Estimate ensemble prediction based on list of probs cubes
sits_combine_predictions.uncertainty | Estimate ensemble prediction based on list of probs cubes
sits_confidence_sampling | Suggest high confidence samples to increase the training set.
sits_config | Configure parameters for sits package
sits_configuration | Configure parameters for sits package
sits_config_show | Configure parameters for sits package
sits_cube | Create data cubes from image collections
sits_cube.local_cube | Create data cubes from image collections
sits_cube.stac_cube | Create data cubes from image collections
sits_cube_copy | Copy the images of a cube to a local directory
sits_filter | Filter time series and data cubes
sits_filters | Filter time series and data cubes
sits_formula_linear | Define a linear formula for classification models
sits_formula_logref | Define a loglinear formula for classification models
sits_geo_dist | Compute the minimum distances among samples and prediction points.
sits_get_data | Get time series from data cubes and cloud services
sits_get_data.csv | Get time series from data cubes and cloud services
sits_get_data.data.frame | Get time series from data cubes and cloud services
sits_get_data.default | Get time series from data cubes and cloud services
sits_get_data.sf | Get time series from data cubes and cloud services
sits_get_data.shp | Get time series from data cubes and cloud services
sits_get_data.sits | Get time series from data cubes and cloud services
sits_impute_linear | Replace NA values with linear interpolation
sits_kfold_validate | Cross-validate time series samples
sits_labels | Get labels associated to a data set
sits_labels.patterns | Get labels associated to a data set
sits_labels.raster_cube | Get labels associated to a data set
sits_labels.sits | Get labels associated to a data set
sits_labels.sits_model | Get labels associated to a data set
sits_labels<- | Change the labels of a set of time series
sits_labels<-.class_cube | Change the labels of a set of time series
sits_labels<-.probs_cube | Change the labels of a set of time series
sits_labels<-.sits | Change the labels of a set of time series
sits_labels_summary | Inform label distribution of a set of time series
sits_labels_summary.sits | Inform label distribution of a set of time series
sits_label_classification | Build a labelled image from a probability cube
sits_label_classification.probs_cube | Build a labelled image from a probability cube
sits_lighttae | Train a model using Lightweight Temporal Self-Attention Encoder
sits_list_collections | Configure parameters for sits package
sits_merge | Merge two data sets (time series or cubes)
sits_merge.raster_cube | Merge two data sets (time series or cubes)
sits_merge.sits | Merge two data sets (time series or cubes)
sits_mixture_model | Multiple endmember spectral mixture analysis
sits_mixture_model.raster_cube | Multiple endmember spectral mixture analysis
sits_mixture_model.sits | Multiple endmember spectral mixture analysis
sits_mlp | Train multi-layer perceptron models using torch
sits_model_export | Export classification models
sits_model_export.sits_model | Export classification models
sits_mosaic | Mosaic classified cubes
sits_patterns | Find temporal patterns associated to a set of time series
sits_reclassify | Reclassify a classified cube
sits_reclassify.class_cube | Reclassify a classified cube
sits_reduce_imbalance | Reduce imbalance in a set of samples
sits_regularize | Build a regular data cube from an irregular one
sits_resnet | Train ResNet classification models
sits_rfor | Train random forest models
sits_run_examples | Informs if sits examples should run
sits_run_tests | Informs if sits tests should run
sits_sample | Sample a percentage of a time series
sits_select | Filter bands on a data set (tibble or cube)
sits_select.patterns | Filter bands on a data set (tibble or cube)
sits_select.raster_cube | Filter bands on a data set (tibble or cube)
sits_select.sits | Filter bands on a data set (tibble or cube)
sits_sgolay | Filter time series and data cubes
sits_smooth | Smooth probability cubes with spatial predictors
sits_som | Use SOM for quality analysis of time series samples
sits_som_clean_samples | Cleans the samples based on SOM map information
sits_som_evaluate_cluster | Evaluate cluster
sits_som_map | Use SOM for quality analysis of time series samples
sits_svm | Train support vector machine models
sits_tae | Train a model using Temporal Self-Attention Encoder
sits_tempcnn | Train temporal convolutional neural network models
sits_timeline | Get timeline of a cube or a set of time series
sits_to_csv | Export a sits tibble metadata to the CSV format
sits_to_xlsx | Save accuracy assessments as Excel files
sits_train | Train classification models
sits_tuning | Tuning machine learning models hyper-parameters
sits_tuning_hparams | Tuning machine learning models hyper-parameters
sits_uncertainty | Estimate classification uncertainty based on probs cube
sits_uncertainty.entropy | Estimate classification uncertainty based on probs cube
sits_uncertainty.least | Estimate classification uncertainty based on probs cube
sits_uncertainty.margin | Estimate classification uncertainty based on probs cube
sits_uncertainty_sampling | Suggest samples for enhancing classification accuracy
sits_validate | Validate time series samples
sits_values | Return the values of a set of time series
sits_variance | Calculate the variance of a probability cube
sits_view | View data cubes and samples in leaflet
sits_view.class_cube | View data cubes and samples in leaflet
sits_view.default | View data cubes and samples in leaflet
sits_view.probs_cube | View data cubes and samples in leaflet
sits_view.raster_cube | View data cubes and samples in leaflet
sits_view.sits | View data cubes and samples in leaflet
sits_view.som_map | View data cubes and samples in leaflet
sits_whittaker | Filter time series and data cubes
sits_xgboost | Train extreme gradient boosting models

## -- misc --

%>% | Pipe
_PACKAGE | sits
`sits_labels<-` | Change the labels of a set of time series