Man pages for sits
Satellite Image Time Series Analysis for Earth Observation Data Cubes
| cerrado_2classes | Samples of classes Cerrado and Pasture |
| dot-check_date_parameter | Check is date is valid |
| 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_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 |
| print.sits_accuracy | Print the values of a confusion matrix |
| print.sits_area_accuracy | Print the area-weighted accuracy |
| samples_l8_rondonia_2bands | Samples of Amazon tropical forest biome for deforestation... |
| samples_modis_ndvi | Samples of nine classes for the state of Mato Grosso |
| sits_accuracy | Assess classification accuracy (area-weighted method) |
| sits_accuracy_summary | Print accuracy summary |
| 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_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_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... |
| 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_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... |
| sits_get_data | Get time series from data cubes and cloud services |
| sits_kfold_validate | Cross-validate time series samples |
| sits_label_classification | Build a labelled image from a probability cube |
| sits_labels | Get labels associated to a data set |
| sits_labels_summary | Inform label distribution of a set of time series |
| sits_lighttae | Train a model using Lightweight Temporal Self-Attention... |
| sits_list_collections | List the cloud collections supported by sits |
| sits_merge | Merge two data sets (time series or cubes) |
| 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-package | sits |
| 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_predictors | Obtain predictors for time series samples |
| sits_pred_normalize | Normalize predictor values |
| sits_pred_reference | Obtain categorical id and predictor labels for time series... |
| sits_pred_sample | Obtain a fraction of the predictors data frame |
| sits_reclassify | 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_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_sgolay | Filter time series with Savitzky-Golay filter |
| sits_show_prediction | Shows the predicted labels for a classified tibble |
| sits_slic | Segment an image using SLIC |
| sits_smooth | 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_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_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 |
| tick-sits_labels-set-tick | Change the labels of a set of time series |
