R: Satellite Image Time Series Analysis for Remote Sensing Data Cubes

# Satellite Image Time Series Analysis for Remote Sensing Data Cubes

## Documentation for package ‘sits’ version 0.16.1

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

## Help Pages

sits-package | sits
%>% | Pipe
.raster_gdal_datatypes | Match sits data types to GDAL data types
:= | Set by reference in data.table
cerrado_2classes | Samples of classes Cerrado and Pasture
plot | Plot time series
plot.classified_image | Plot classified images
plot.keras_model | Plot Keras (deep learning) model
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.sits | Plot time series
plot.som_evaluate_cluster | Plot confusion between clusters
plot.som_map | Plot a SOM map
plot.uncertainty_cube | Plot uncertainty cubes
point_mt_6bands | A time series sample with data from 2000 to 2016
samples_l8_rondonia_2bands | Samples of Amazon tropical forest biome for deforestation analysis
samples_modis_4bands | Samples of nine classes for the state of Mato Grosso
sits | sits
sits_accuracy | Assess classification accuracy (area-weighted method)
sits_accuracy.classified_image | Assess classification accuracy (area-weighted method)
sits_accuracy.sits | Assess classification accuracy (area-weighted method)
sits_bands | Get the names of the bands
sits_bbox | 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 | Find clusters in time series samples
sits_cluster_dendro | Find clusters in time series samples
sits_cluster_frequency | Find clusters in time series samples
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.satveg_cube | Create data cubes from image collections
sits_cube.stac_cube | Create data cubes from image collections
sits_cube.wtss_cube | Create data cubes from image collections
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_get_data | Get time series from data cubes and cloud services
sits_get_data.csv_raster_cube | Get time series from data cubes and cloud services
sits_get_data.csv_satveg_cube | Get time series from data cubes and cloud services
sits_get_data.csv_wtss_cube | Get time series from data cubes and cloud services
sits_get_data.raster_cube | Get time series from data cubes and cloud services
sits_get_data.satveg_cube | Get time series from data cubes and cloud services
sits_get_data.shp_raster_cube | Get time series from data cubes and cloud services
sits_get_data.shp_satveg_cube | Get time series from data cubes and cloud services
sits_get_data.shp_wtss_cube | Get time series from data cubes and cloud services
sits_get_data.wtss_cube | 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<- | Change the labels of a set of time series
sits_labels_summary | Inform label distribution of a set of time series
sits_label_classification | Build a labelled image from a probability cube
sits_list_collections | Configure parameters for sits package
sits_merge | Merge two data sets (time series or cubes)
sits_metadata_to_csv | Export a sits tibble metadata to the CSV format
sits_mlp | Train multi-layer perceptron models
sits_mlr | Train multinomial log-linear models
sits_patterns | Find temporal patterns associated to a set of time series
sits_regularize | Build a regular data cube from an irregular one
sits_ResNet | Train ResNet classification models
sits_rfor | Train random forest models
sits_sample | Sample a percentage of a time series
sits_select | 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_smooth.bayes | Smooth probability cubes with spatial predictors
sits_smooth.bilateral | Smooth probability cubes with spatial predictors
sits_smooth.gaussian | Smooth probability cubes with spatial predictors
sits_som | Use SOM for quality analysis of time series samples
sits_som_clean_samples | Use SOM for quality analysis of time series samples
sits_som_evaluate_cluster | Use SOM for quality analysis of time series samples
sits_som_map | Use SOM for quality analysis of time series samples
sits_svm | Train support vector machine models
sits_TempCNN | Train temporal convolutional neural network models
sits_timeline | Get timeline of a cube or a set of time series
sits_time_series | Get the time series for a row of a sits tibble
sits_to_xlsx | Save accuracy assessments as Excel files
sits_to_zoo | Export time series to zoo format
sits_train | Train classification models
sits_twdtw_classify | Find matches between patterns and time series using TWDTW
sits_uncertainty | Estimate classification uncertainty based on probs cube
sits_uncertainty.entropy | Estimate classification uncertainty based on probs cube
sits_values | Return the values of a set of time series
sits_view | View data cubes and samples in leaflet
sits_view.classified_image | 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_whittaker | Filter time series and data cubes
sits_xgboost | Train extreme gradient boosting models
_PACKAGE | sits
`sits_labels<-` | Change the labels of a set of time series