sits1.5.1 package

Satellite Image Time Series Analysis for Earth Observation Data Cubes

Cross-validate time series samples

Print the values of a confusion matrix

Print the area-weighted accuracy

Print accuracy summary

Check is date is valid

histogram of prob cubes

histogram of data cubes

Histogram

Histogram uncertainty cubes

Replace NA values with 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

Assess classification accuracy (area-weighted method)

Plot probability cubes

Plot probability vector cubes

Plot RGB data cubes

Plot time series

Plot Random Forest model

Function to show colors in SITS

Plot SAR data cubes

Plot confusion matrix

Plot a dendrogram cluster

Plot confusion between clusters

Plot a SOM map

Plot Torch (deep learning) model

Function to retrieve sits color table

Plot uncertainty cubes

Plot uncertainty vector cubes

Plot variance cubes

Plot RGB vector data cubes

Plot XGB model

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.

Get the names of the bands

Get the bounding box of the data

Classify time series or data cubes

Create a closure for calling functions with and without data

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

Train multi-layer perceptron models using torch

Estimate ensemble prediction based on list of probs cubes

Suggest high confidence samples to increase the training set.

Show current sits configuration

List the cloud collections supported by sits

Configure parameters for sits package

Copy the images of a cube to a local directory

Create data cubes from image collections

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 time series from data cubes and cloud services

Replace NA values in time series with imputation function

Build a labelled image from a probability cube

Inform label distribution of a set of time series

Get labels associated to a data set

Train a model using Lightweight Temporal Self-Attention Encoder

List the cloud collections supported by sits

Merge two data sets (time series or cubes)

Convert MGRS tile information to ROI in WGS84

Multiple endmember spectral mixture analysis

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 random forest models

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 bands on a data set (tibble or cube)

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

Evaluate cluster

Use SOM for quality analysis of time series samples

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

Get timeline of a cube or a set of time series

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

Summarise variance cubes

Change the labels of a set of time series

An end-to-end toolkit for land use and land cover classification using big Earth observation data, based on machine learning methods applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>. Builds regular data cubes from collections in AWS, Microsoft Planetary Computer, Brazil Data Cube, Copernicus Data Space Environment (CDSE), Digital Earth Africa, Digital Earth Australia, NASA HLS using the Spatio-temporal Asset Catalog (STAC) protocol (<https://stacspec.org/>) and the 'gdalcubes' R package developed by Appel and Pebesma (2019) <doi:10.3390/data4030092>. Supports visualization methods for images and time series and smoothing filters for dealing with noisy time series. Includes functions for quality assessment of training samples using self-organized maps as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>. Provides machine learning methods including support vector machines, random forests, extreme gradient boosting, multi-layer perceptrons, temporal convolutional neural networks proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>, and temporal attention encoders by Garnot and Landrieu (2020) <doi:10.48550/arXiv.2007.00586>. Supports GPU processing of deep learning models using torch <https://torch.mlverse.org/>. Performs efficient classification of big Earth observation data cubes and includes functions for post-classification smoothing based on Bayesian inference, and methods for active learning and uncertainty assessment. Supports object-based time series analysis using package supercells <https://jakubnowosad.com/supercells/>. Enables best practices for estimating area and assessing accuracy of land change as recommended by Olofsson et al (2014) <doi:10.1016/j.rse.2014.02.015>. Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Maintainer: Gilberto Camara License: GPL-2 Last published: 2024-08-19

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