OTBsegm

https://github.com/cidree/otbsegm

Repository

README.Rmd

---
output: github_document
---



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

# OTBsegm 

[![CRAN status](https://www.r-pkg.org/badges/version/OTBsegm)](https://CRAN.R-project.org/package=OTBsegm)
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[![Codecov test coverage](https://codecov.io/gh/Cidree/OTBsegm/graph/badge.svg)](https://app.codecov.io/gh/Cidree/OTBsegm)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
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[![Last Month Downloads](https://cranlogs.r-pkg.org/badges/last-month/OTBsegm?color=green)](https://CRAN.R-project.org/package=OTBsegm)


**OTBsegm** is an R package that provides a user-friendly interface to the unsupervised image segmentation algorithms available in [Orfeo ToolBox (OTB)](https://www.orfeo-toolbox.org/), a powerful open-source library for remote sensing image processing. **OTBsegm** is built on top of [`link2GI`](https://r-spatial.github.io/link2GI/) R package, providing easy access to image segmentation algorithms.

To use {`OTBsegm`}, you must first install OTB on your machine. Once OTB is installed and properly linked through {`link2GI`} (see examples), this package allows you to easily integrate OTB's segmentation algorithms into your workflows.


## Installation

You can install the development version of OTBsegm from [GitHub](https://github.com/) with:

``` r
# install.packages("pak")
pak::pak("Cidree/OTBsegm")
```

## Example

We will see how to segment an image included in the package:

```{r}
## load packages
library(link2GI)
library(OTBsegm)
library(terra)

## load image
image_sr <- rast(system.file("raster/pnoa.tiff", package = "OTBsegm"))

## visualize
plotRGB(image_sr)
```

The image is a 500x500 meters RGB tile, with a spatial resolution of 15 cm in Galicia, Spain. The meanshift algorithm has the next important arguments:

* **spatialr**: spatial radius of the neighborhood

* **ranger**: range radius defining the radius (expressed in radiometry unit) in the multispectral space

* **minsize**: minimum size of a region (in pixel unit) in segmentation. Smaller clusters will be merged to the neighboring cluster with the closest radiometry. If set to 0 no pruning is done. The image's resolution is 1.2 m, therefore, a value of `minsize = 10` means that the smallest segment will be $10 * 1.2^2 = 14.4 m^2$.

In order to use the algorithms, we need to link our OTB installation using {`link2GI`}:

```{r}
otblink <- link2GI::linkOTB(searchLocation = "C:/OTB/")
```

Once we are connected, we can apply the segmentation algorithm and visualize the results:

```{r}
results_ms_sf <- segm_meanshift(
    image    = image_sr,
    otb      = otblink,
    spatialr = 5,
    ranger   = 25,
    maxiter  = 10,
    minsize  = 10
)
```

```{r}
plotRGB(image_sr)
plot(sf::st_geometry(results_ms_sf), add = TRUE)
```

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