# hyperslic

## Description

The simple linear iterative clustering (SLIC) algorithm performs superpixel
oversegmentation of images. While the `superpixels`

and `superpixels3`

functions apply SLIC to 2-D
grayscale or RGB images and 3-D volumes, respectively, they are not suitable for use on
hyperspectral images, because hyperspectral images have a large number of spectral bands.
Using `superpixels`

on the just three spectral bands of
the hyperspectral image may not capture the information in the several other spectral bands of
the hyperspectral image. The `hyperslic`

function extends the SLIC algorithm
to 2-D superpixel segmentation of hyperspectral images by considering the information in these
spectral bands. You can use the superpixel regions provided by the
`hyperslic`

function to reduce the complexity of further
segmentation.

`[`

fine-tunes the behavior of the function using one or more optional name-value arguments. For
example, `L`

,`numLabels`

] = hyperslic(`hCube`

,`K`

,`Name=Value`

)`NumIterations=20`

specifies to perform 20 iterations during the
clustering phase of the SLIC algorithm.

**Note**

This function requires the Image Processing Toolbox™ Hyperspectral Imaging Library. You can install the Image Processing Toolbox Hyperspectral Imaging Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

The Image Processing Toolbox Hyperspectral Imaging Library requires desktop MATLAB^{®}, as MATLAB
Online™ or MATLAB
Mobile™ do not support the library.

## Examples

## Input Arguments

## Output Arguments

## Algorithms

The `hyperslic`

function extends the SLIC algorithm to 2-D superpixel
segmentation of hyperspectral images by considering the information in the spectral bands. To
improve the speed of the SLIC algorithm for hyperspectral images without losing much spectral
information, the `hyperslic`

function preprocesses the specified
hyperspectral image to reduce its spectral dimensions method before using the extended SLIC
algorithm for segmentation. However, if the number of spectral bands, after spectral
dimensionality reduction, is fewer than three, the `hyperslic`

function
performs the 2-D superpixel oversegmentation by using the `superpixels`

function on the mean image along the spectral dimension.

## References

[1] Achanta, R., A. Shaji, K. Smith,
A. Lucchi, P. Fua, and Sabine Süsstrunk. “SLIC Superpixels Compared to State-of-the-Art
Superpixel Methods.” *IEEE Transactions on Pattern Analysis and Machine
Intelligence* 34, no. 11 (November 2012): 2274–82.
https://doi.org/10.1109/TPAMI.2012.120.

[2] Xu, Xiang, Jun Li, Changshan Wu,
and Antonio Plaza. “Regional Clustering-Based Spatial Preprocessing for Hyperspectral
Unmixing.” *Remote Sensing of Environment* 204 (January
2018): 333–46. https://doi.org/10.1016/j.rse.2017.10.020.

## Version History

**Introduced in R2023b**