# Satellite Image Time Series Analysis for Earth Observation Data Cubes

## Documentation for package ‘sits’ version 1.4.2-1

- 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.class_vector_cube | Plot Segments
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.probs_vector_cube | Plot probability vector 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.sits_cluster | Plot a dendrogram cluster
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.vector_cube | Plot RGB vector data 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.default | Assess classification accuracy (area-weighted method)
sits_accuracy.derived_cube | Assess classification accuracy (area-weighted method)
sits_accuracy.raster_cube | Assess classification accuracy (area-weighted method)
sits_accuracy.sits | Assess classification accuracy (area-weighted method)
sits_accuracy.tbl_df | Assess classification accuracy (area-weighted method)
sits_apply | Apply a function on a set of time series
sits_apply.default | Apply a function on a set of time series
sits_apply.derived_cube | 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_apply.tbl_df | 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.default | 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_bands.tbl_df | Get the names of the bands
sits_bands<- | Get the names of the bands
sits_bands<-.default | 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_bbox | Get the bounding box of the data
sits_bbox.default | 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_bbox.tbl_df | Get the bounding box of the data
sits_classify | Classify time series or data cubes
sits_classify.default | Classify time series or data cubes
sits_classify.derived_cube | Classify time series or data cubes
sits_classify.raster_cube | Classify time series or data cubes
sits_classify.segs_cube | Classify time series or data cubes
sits_classify.sits | Classify time series or data cubes
sits_classify.tbl_df | Classify time series or data cubes
sits_cluster_clean | Removes labels that are minority in each cluster.
sits_cluster_dendro | Find clusters in time series samples
sits_cluster_dendro.default | Find clusters in time series samples
sits_cluster_dendro.sits | Find clusters in time series samples
sits_cluster_dendro.tbl_df | 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_qgis | Function to save color table as QML style for data cube
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_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.default | 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_config_show | Show current sits configuration
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_factory_function | Create a closure for calling functions with and without data
sits_filter | Filter time series with smoothing filter
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_kfold_validate | Cross-validate time series samples
sits_labels | Get labels associated to a data set
sits_labels.default | Get labels associated to a data set
sits_labels.derived_cube | Get labels associated to a data set
sits_labels.derived_vector_cube | 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.tbl_df | 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<-.default | 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<-.tbl_df | Get labels associated to a data set
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.default | Build a labelled image from a probability cube
sits_label_classification.derived_cube | Build a labelled image from a probability cube
sits_label_classification.probs_cube | Build a labelled image from a probability cube
sits_label_classification.probs_vector_cube | Build a labelled image from a probability cube
sits_label_classification.raster_cube | Build a labelled image from a probability cube
sits_label_classification.tbl_df | Build a labelled image from a probability cube
sits_lighttae | Train a model using Lightweight Temporal Self-Attention Encoder
sits_list_collections | List the cloud collections supported by sits
sits_merge | Merge two data sets (time series or cubes)
sits_merge.default | 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.default | Multiple endmember spectral mixture analysis
sits_mixture_model.derived_cube | 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_mixture_model.tbl_df | 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_predictors | Obtain predictors for time series samples
sits_pred_features | Obtain numerical values of predictors for time series samples
sits_pred_normalize | Normalize predictor values
sits_pred_reference | Obtain categorical id and predictor labels for time series samples
sits_pred_references | Obtain categorical id and predictor labels for time series samples
sits_pred_sample | Obtain a fraction of the predictors data frame
sits_reclassify | Reclassify a classified cube
sits_reclassify.class_cube | Reclassify a classified cube
sits_reclassify.default | Reclassify a classified cube
sits_reclassify.tbl_df | 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_regularize.default | Build a regular data cube from an irregular one
sits_regularize.derived_cube | Build a regular data cube from an irregular one
sits_regularize.mpc_cube_sentinel-1-grd | Build a regular data cube from an irregular one
sits_regularize.raster_cube | Build a regular data cube from an irregular one
sits_regularize.tbl_df | 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_segment | Segment an image
sits_select | Filter bands on a data set (tibble or cube)
sits_select.default | 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_select.tbl_df | Filter bands on a data set (tibble or cube)
sits_sgolay | Filter time series with Savitzky-Golay filter
sits_slic | Segment an image using SLIC
sits_smooth | Smooth probability cubes with spatial predictors
sits_smooth.default | Smooth probability cubes with spatial predictors
sits_smooth.derived_cube | Smooth probability cubes with spatial predictors
sits_smooth.probs_cube | Smooth probability cubes with spatial predictors
sits_smooth.raster_cube | Smooth probability cubes with spatial predictors
sits_smooth.tbl_df | 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_stats | Obtain statistics for all sample bands
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_timeline.default | Get timeline of a cube or a set of time series
sits_timeline.derived_cube | Get timeline of a cube or a set of time series
sits_timeline.raster_cube | Get timeline of a cube or a set of time series
sits_timeline.sits | Get timeline of a cube or a set of time series
sits_timeline.sits_model | Get timeline of a cube or a set of time series
sits_timeline.tbl_df | 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_csv.default | Export a sits tibble metadata to the CSV format
sits_to_csv.sits | Export a sits tibble metadata to the CSV format
sits_to_csv.tbl_df | 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.default | 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_variance | Calculate the variance of a probability cube
sits_variance.default | Calculate the variance of a probability cube
sits_variance.derived_cube | Calculate the variance of a probability cube
sits_variance.probs_cube | Calculate the variance of a probability cube
sits_variance.raster_cube | Calculate the variance of a probability cube
sits_variance.tbl_df | 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.data.frame | 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_view.uncertainty_cube | View data cubes and samples in leaflet
sits_view.vector_cube | View data cubes and samples in leaflet
sits_whittaker | Filter time series with whittaker filter
sits_xgboost | Train extreme gradient boosting models
summary.class_cube | Summarize data cubes
summary.raster_cube | Summarize data cubes
summary.sits | Summarize sits
summary.sits_accuracy | Summarize accuracy matrix for training data
summary.sits_area_accuracy | Summarize accuracy matrix for area data

## -- misc --

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