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 0.12.0

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

## Help Pages

C P S T misc

sits-package | sits

## -- C --

cerrado_2classes | Samples of classes Cerrado and Pasture

## -- P --

plot | Generic interface for ploting time series
plot.classified_image | Generic interface for ploting classified images
plot.keras_model | Generic interface for plotting a Keras model
plot.patterns | Generic interface for ploting patterns
plot.predicted | Generic interface for ploting time series predictions
plot.probs_cube | Generic interface for plotting probability cubes
plot.raster_cube | Generic interface for plotting stack cubes
plot.sits | Generic interface for ploting time series
plot.som_evaluate_cluster | Plot information about confusion between clusters
plot.som_map | Generic interface for plotting a SOM map
point_mt_6bands | A time series sample with data from 2000 to 2016

## -- S --

samples_modis_4bands | Samples of nine classes for the state of Mato Grosso
samples_mt_6bands | Samples of nine classes for the state of Mato Grosso
sits | sits
sits_accuracy | Area-weighted classification accuracy assessment
sits_accuracy.classified_image | Area-weighted classification accuracy assessment
sits_accuracy.sits | Area-weighted classification accuracy assessment
sits_apply | Apply a function over a time series.
sits_bands | Informs the names of the bands
sits_bands<- | Replaces the names of the bands
sits_bbox | Get the bounding box of the data
sits_classify | Classify time series or data cube using machine learning models
sits_classify.raster_cube | Classify time series or data cube using machine learning models
sits_classify.sits | Classify time series or data cube using machine learning models
sits_cluster_clean | Cluster cleaner
sits_cluster_dendro | Clusters a set of time series using aglomerative hierarchical clustering
sits_cluster_frequency | Cluster contigency table
sits_config | Reads a configuration file and loads it in the main environment
sits_config_info | Information about configuration file
sits_config_show | Shows the contents of the sits configuration file
sits_create_folds | Create partitions of a data set
sits_cube | Defines a data cube
sits_cube.aws_cube | Defines a data cube
sits_cube.bdc_cube | Defines a data cube
sits_cube.deafrica_cube | Defines a data cube
sits_cube.local_cube | Defines a data cube
sits_cube.probs_cube | Defines a data cube
sits_cube.satveg_cube | Defines a data cube
sits_cube_copy | Creates the contents of a data cube
sits_data_to_csv | Export a sits tibble data to the CSV format
sits_envelope | Envelope filter
sits_filter | General function for filtering
sits_formula_linear | Define a linear formula for classification models
sits_formula_logref | Define a loglinear formula for classification models
sits_from_zoo | Import time series in the zoo format to a sits tibble
sits_get_data | Obtain time series from different sources
sits_get_data.csv_raster_cube | Obtain time series from different sources
sits_get_data.csv_satveg_cube | Obtain time series from different sources
sits_get_data.raster_cube | Obtain time series from different sources
sits_get_data.satveg_cube | Obtain time series from different sources
sits_get_data.shp_raster_cube | Obtain time series from different sources
sits_get_data.shp_satveg_cube | Obtain time series from different sources
sits_impute_linear | Linear imputation of NA values using C++ implementation
sits_interp | Interpolation function of the time series of a sits_tibble
sits_keras_diagnostics | Diagnostic information about a Keras deep learning model
sits_kfold_validate | Cross-validate temporal patterns
sits_labels | Returns the information about labels of a data set (tibble or cube)
sits_labels<- | Change labels of a sits tibble
sits_labels_summary | Returns the information about labels of a tibble data set
sits_label_classification | Post-process a classified data raster probs to obtain a labelled image
sits_label_majority | Post-process a classified data raster with a majority filter
sits_lda | Train a sits classification model using linear discriminant analysis
sits_linear_interp | Interpolation function of the time series in a sits tibble
sits_merge | Merge two data sets (time series or cubes)
sits_metadata_to_csv | Export a sits tibble metadata to the CSV format
sits_missing_values | Remove missing values
sits_mlp | Train a deep learning model using multi-layer perceptron
sits_mlr | Train a sits classification model using multinomial log-linear
sits_ndwi | Builds normalized difference water index
sits_patterns | Create temporal patterns using a generalised additive model (gam)
sits_qda | Train a classification model using quadratic discriminant analysis
sits_ranger | Train a sits classifiction model using fast random forest algorithm
sits_regularize | Creates a regularized data cube from an irregular one
sits_ResNet | Train a model using the ResNet model
sits_rfor | Train a SITS classifiction model using random forest algorithm
sits_sample | Sample a percentage of a time series
sits_savi | Builds soil-adjusted vegetation index
sits_select | Filter bands on a data set (tibble or cube)
sits_sgolay | Smooth the time series using Savitsky-Golay filter
sits_smooth | Post-process a classified data raster probs using smoothing
sits_smooth.bayes | Post-process a classified data raster probs using smoothing
sits_smooth.bilateral | Post-process a classified data raster probs using smoothing
sits_smooth.gaussian | Post-process a classified data raster probs using smoothing
sits_som_clean_samples | Clean samples
sits_som_cluster | Clustering a set of satellite image time series using SOM
sits_som_evaluate_cluster | Evaluate cluster
sits_som_map | Generate a Kohonen map for sample quality control
sits_svm | Train a sits classification model using a support vector machine
sits_TempCNN | Train a model using the Temporal Convolutional Neural Network
sits_timeline | Obtains the timeline
sits_time_series | Retrieve time series for a row of a sits tibble
sits_to_xlsx | Saves the results of accuracy assessments as Excel files
sits_to_zoo | Export data to be used to the zoo format
sits_train | Train sits classification models
sits_twdtw_classify | Find matches between patterns and time series using TWDTW
sits_values | Return the values of a given sits tibble as a list of matrices.
sits_view | Generic interface for visualization of data cube
sits_view.classified_image | Generic interface for visualization of data cube
sits_view.raster_cube | Generic interface for visualization of data cube
sits_whittaker | Filter the time series using Whittaker smoother
sits_xgboost | Train a model with an extreme gradient boosting machine

## -- T --

timeline_2000_2017 | The timeline for the sequence of images for MOD13Q1 collection 6
timeline_2013_2014 | The timeline for the sequence of images one year (2013 to 2014)
ts_zoo | A time series in the ZOO format

## -- misc --

%>% | Pipe
:= | Set by reference in data.table
_PACKAGE | sits
`sits_bands<-` | Replaces the names of the bands
`sits_labels<-` | Change labels of a sits tibble