README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  comment = "#>",
  out.width = "100%",
  fig.path = "man/figures/README-"
)
```

# sitsfeats - Satellite Image Time Series Features <img src="inst/extdata/img/logo.png" align="right" width="120"/>
R package for extracting metrics from time series of satellite images

<!-- badges: start -->
[![R-CMD-check](https://github.com/oldlipe/sitsfeats/workflows/R-CMD-check/badge.svg)](https://github.com/oldlipe/sitsfeats/actions)
[![Codecov test coverage](https://codecov.io/gh/OldLipe/sitsfeats/branch/master/graph/badge.svg)](https://codecov.io/gh/OldLipe/sitsfeats?branch=master)
[![CRAN](http://www.r-pkg.org/badges/version/sitsfeats)](https://CRAN.R-project.org/package=sitsfeats)
[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
<!-- badges: end -->

The goal of the sitsfeats package is to extract temporal metrics from satellite images time series. The metrics implemented in this version are:
  
  - **basics**: statistics metrics
  - **polar**: polar metrics
  
  
The implementations of this package are based on the [stmetrics](https://github.com/brazil-data-cube/stmetrics) library in Python.

## Installation

You can install the CRAN version with:

``` r
install.packages("sitsfeats")
```

Or the development version of sitsfeats from [github](https://github.com/OldLipe/sitsfeats) with:

``` r
library(devtools)
devtools::install_github("oldlipe/sitsfeats")
```

## How to use?

### Polar metrics example
```{r}
# loading sitsfeats and tibble packages
library(sitsfeats)
library(tibble)

# loading example time series
data("timeseries")

polar_metrics <- tibble::tibble(area_q1 = area_q1(timeseries),
                                area_q2 = area_q2(timeseries),
                                area_q3 = area_q3(timeseries),
                                area_q4 = area_q3(timeseries))

polar_metrics
```

### Polar metrics visualization

```{r, fig.align="center", fig.height=5, fig.width=9, dpi=300}
# get one instance from timeseries
one_instance <- timeseries[1,]

# plot first time series example
polar_plot(one_instance)
```

## Getting help

Feel free to create issues if you have any questions.