Package index • sits

All functions

cerrado_2classes: Samples of classes Cerrado and Pasture

hist(<probs_cube>): histogram of prob cubes

hist(<raster_cube>): histogram of data cubes

hist(<sits>): Histogram

hist(<uncertainty_cube>): Histogram uncertainty cubes

impute_linear(): Replace NA values by linear interpolation

plot(<sits>): Plot time series and data cubes

plot(<class_cube>): Plot classified images

plot(<class_vector_cube>): Plot Segments

plot(<dem_cube>): Plot DEM cubes

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(<sar_cube>): Plot SAR data cubes

plot(<sits_accuracy>): Plot confusion matrix

plot(<sits_cluster>): Plot a dendrogram cluster

plot(<som_clean_samples>): Plot SOM samples evaluated

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(<uncertainty_vector_cube>): Plot uncertainty vector 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

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-package sits: sits

sits_accuracy(): Assess classification accuracy

sits_add_base_cube(): Add base maps to a time series data cube

sits_apply(): 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_stars(): Convert a data cube into a stars object

sits_as_terra(): Convert a data cube into a Spatial Raster object from terra

sits_bands() `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 a regular raster cube

sits_classify(<vector_cube>): Classify a segmented data cube

sits_classify(<sits>): Classify a set of time series

sits_clean(): Cleans a classified map using a local window

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_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_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_config_user_file(): Create a user configuration file.

sits_cube(): Create data cubes from image collections

sits_cube(<local_cube>): Create sits cubes from cubes in flat files in a local

sits_cube(<results_cube>): Create a results cube from local files

sits_cube(<stac_cube>): Create data cubes from image collections accessible by STAC

sits_cube(<vector_cube>): Create a vector cube from local files

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_class(): Get values from classified maps

sits_get_data(): Get time series from data cubes and cloud services

sits_get_data(<csv>): Get time series using CSV files

sits_get_data(<data.frame>): Get time series using sits objects

sits_get_data(<sf>): Get time series using sf objects

sits_get_data(<shp>): Get time series using shapefiles

sits_get_data(<sits>): Get time series using sits objects

sits_get_probs(): Get values from probability maps

sits_impute(): Replace NA values in time series with imputation function

sits_kfold_validate(): Cross-validate time series samples

sits_label_classification(): Build a labelled image from a probability cube

`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<-`(): Change the labels of a set of time series

sits_labels(): Get labels associated to a data set

sits_labels_summary(): Inform label distribution of a set of time series

sits_lightgbm(): Train light gradient boosting model

sits_lighttae(): Train a model using Lightweight Temporal Self-Attention Encoder

sits_list_collections(): List the cloud collections supported by sits

sits_lstm_fcn(): Train a Long Short Term Memory Fully Convolutional Network

sits_merge(): Merge two data sets (time series or cubes)

sits_mgrs_to_roi(): Convert MGRS tile information to ROI in WGS84

sits_mixture_model(): Multiple endmember spectral mixture analysis

sits_mlp(): Train multi-layer perceptron models using torch

sits_model_export(): Export classification models

sits_mosaic(): Mosaic classified cubes

sits_patterns(): Find temporal patterns associated to a set of time series

sits_pred_features(): Obtain numerical values of predictors for time series samples

sits_pred_normalize(): Normalize predictor values

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_predictors(): Obtain predictors for time series samples

sits_reclassify(): Reclassify a classified cube

sits_reduce(): Reduces a cube or samples from a summarization function

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_roi_to_mgrs(): Given a ROI, find MGRS tiles intersecting it.

sits_roi_to_tiles(): Find tiles of a given ROI and Grid System

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_sampling_design(): Allocation of sample size to strata

sits_segment(): Segment an image

sits_select(): Filter a data set (tibble or cube) for bands, tiles, and dates

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_som_clean_samples(): Cleans the samples based on SOM map information

sits_som_evaluate_cluster(): Evaluate cluster

sits_som_map(): Build a SOM for quality analysis of time series samples

sits_som_remove_samples(): Evaluate cluster

sits_stats(): Obtain statistics for all sample bands

sits_stratified_sampling(): Allocation of sample size to strata

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_texture(): Apply a set of texture measures on a data cube.

sits_tiles_to_roi(): Convert MGRS tile information to ROI in WGS84

sits_timeline(): Get timeline of a cube or a set of time series

sits_timeseries_to_csv(): Export a a full sits tibble to the CSV format

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_sampling(): Suggest samples for enhancing classification accuracy

sits_validate(): Validate time series samples

sits_variance(): Calculate the variance of a probability cube

sits_view(): 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

summary(<variance_cube>): Summarize variance cubes