sits1.5.3-1 package

Check is date is valid

histogram of prob cubes

histogram of data cubes

Histogram

Histogram uncertainty cubes

Replace NA values by linear interpolation

Plot classified images

Plot Segments

Plot DEM cubes

Make a kernel density plot of samples distances.

Plot patterns that describe classes

Plot time series predictions

Plot probability cubes

Plot probability vector cubes

Plot RGB data cubes

Plot time series and data cubes

Plot Random Forest model

Plot SAR data cubes

Plot confusion matrix

Plot a dendrogram cluster

Plot SOM samples evaluated

Plot confusion between clusters

Plot a SOM map

Plot Torch (deep learning) model

Plot uncertainty cubes

Plot uncertainty vector cubes

Plot variance cubes

Plot RGB vector data cubes

Plot XGB model

Print the values of a confusion matrix

Print the area-weighted accuracy

Print accuracy summary

Assess classification accuracy

Add base maps to a time series data cube

Apply a function on a set of time series

Return a sits_tibble or raster_cube as an sf object.

Convert a data cube into a stars object

Convert a data cube into a Spatial Raster object from terra

Get the names of the bands

Get the bounding box of the data

Classify a regular raster cube

Classify time series or data cubes

Classify a segmented data cube

Classify a set of time series

Cleans a classified map using a local window

Removes labels that are minority in each cluster.

Find clusters in time series samples

Show label frequency in each cluster produced by dendrogram analysis

Function to save color table as QML style for data cube

Function to reset sits color table

Function to set sits color table

Function to show colors in SITS

Function to retrieve sits color table

Estimate ensemble prediction based on list of probs cubes

Suggest high confidence samples to increase the training set.

Show current sits configuration

Create a user configuration file.

Configure parameters for sits package

Copy the images of a cube to a local directory

Create sits cubes from cubes in flat files in a local

Create data cubes from image collections

Create a results cube from local files

Create data cubes from image collections accessible by STAC

Create a vector cube from local files

Create a closure for calling functions with and without data

Filter time series with smoothing filter

Define a linear formula for classification models

Define a loglinear formula for classification models

Compute the minimum distances among samples and prediction points.

Get values from classified maps

Get time series using CSV files

Get time series using sits objects

Get time series from data cubes and cloud services

Get time series using sf objects

Get time series using shapefiles

Get time series using sits objects

Get values from probability maps

Replace NA values in time series with imputation function

Cross-validate time series samples

Build a labelled image from a probability cube

Inform label distribution of a set of time series

Change the labels of a set of time series

Change the labels of a set of time series

Change the labels of a set of time series

Change the labels of a set of time series

Change the labels of a set of time series

Get labels associated to a data set

Train light gradient boosting model

Train a model using Lightweight Temporal Self-Attention Encoder

List the cloud collections supported by sits

Train a Long Short Term Memory Fully Convolutional Network

Merge two data sets (time series or cubes)

Convert MGRS tile information to ROI in WGS84

Multiple endmember spectral mixture analysis

Train multi-layer perceptron models using torch

Export classification models

Mosaic classified cubes

Find temporal patterns associated to a set of time series

Obtain numerical values of predictors for time series samples

Normalize predictor values

Obtain categorical id and predictor labels for time series samples

Obtain a fraction of the predictors data frame

Obtain predictors for time series samples

Reclassify a classified cube

Reduce imbalance in a set of samples

Reduces a cube or samples from a summarization function

Build a regular data cube from an irregular one

Train ResNet classification models

Train random forest models

Given a ROI, find MGRS tiles intersecting it.

Find tiles of a given ROI and Grid System

Informs if sits examples should run

Informs if sits tests should run

Sample a percentage of a time series

Allocation of sample size to strata

Segment an image

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

Filter time series with Savitzky-Golay filter

Shows the predicted labels for a classified tibble

Segment an image using SLIC

Smooth probability cubes with spatial predictors

Cleans the samples based on SOM map information

Evaluate cluster

Build a SOM for quality analysis of time series samples

Evaluate cluster

Obtain statistics for all sample bands

Allocation of sample size to strata

Train support vector machine models

Train a model using Temporal Self-Attention Encoder

Train temporal convolutional neural network models

Apply a set of texture measures on a data cube.

Convert MGRS tile information to ROI in WGS84

Get timeline of a cube or a set of time series

Export a a full sits tibble to the CSV format

Export a sits tibble metadata to the CSV format

Save accuracy assessments as Excel files

Train classification models

Tuning machine learning models hyper-parameters

Tuning machine learning models hyper-parameters

Suggest samples for enhancing classification accuracy

Estimate classification uncertainty based on probs cube

Validate time series samples

Calculate the variance of a probability cube

View data cubes and samples in leaflet

Filter time series with whittaker filter

Train extreme gradient boosting models

sits

Summarize data cubes

Summarize data cubes

Summarize accuracy matrix for training data

Summarize accuracy matrix for area data

Summarize sits

Summarize variance cubes