Satellite Image Time Series Analysis for Remote Sensing Data Cubes

Documentation for package ‘sits’ version 0.13.0

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
check_functions	Auxiliary check functions
config_show	Shows the contents of the sits configuration file

-- 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_show	Shows the contents of the sits configuration file
sits_create_folds	Create partitions of a data set
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.csv_wtss_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_get_data.shp_wtss_cube	Obtain time series from different sources
sits_get_data.wtss_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
.check_apply	Auxiliary check functions
.check_chr	Auxiliary check functions
.check_chr_choices	Auxiliary check functions
.check_chr_empty	Auxiliary check functions
.check_chr_type	Auxiliary check functions
.check_file	Auxiliary check functions
.check_int_type	Auxiliary check functions
.check_length	Auxiliary check functions
.check_lgl	Auxiliary check functions
.check_lgl_type	Auxiliary check functions
.check_lst	Auxiliary check functions
.check_lst_type	Auxiliary check functions
.check_na	Auxiliary check functions
.check_names	Auxiliary check functions
.check_null	Auxiliary check functions
.check_num	Auxiliary check functions
.check_num_range	Auxiliary check functions
.check_num_type	Auxiliary check functions
.check_that	Auxiliary check functions
.check_warn	Auxiliary check functions
.config	Reads a configuration file and loads it in the main environment
.stac_bands_select	Select stac items by sits bands.
.stac_items_query	Creates a query to send to STAC api
:=	Set by reference in data.table
_PACKAGE	sits
`sits_bands<-`	Replaces the names of the bands
`sits_labels<-`	Change labels of a sits tibble