# Morphological segmentation of hyperspectral images

arXiv: https://arxiv.org/abs/2010.00853 · HTML: https://mlsanity.com/paper/2010.00853/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Triplet-Watershed for Hyperspectral Image Classification](https://mlsanity.com/paper/2103.09384/) (2021)
- [Hierarchical Homogeneity-Based Superpixel Segmentation: Application to Hyperspectral Image Analysis](https://mlsanity.com/paper/2407.15321/) (2024)

## Abstract (arXiv)

The present paper develops a general methodology for the morphological segmentation of hyperspectral images, i.e., with an important number of channels. This approach, based on watershed, is composed of a spectral classification to obtain the markers and a vectorial gradient which gives the spatial information. Several alternative gradients are adapted to the different hyperspectral functions. Data reduction is performed either by Factor Analysis or by model fitting. Image segmentation is done on different spaces: factor space, parameters space, etc. On all these spaces the spatial/spectral segmentation approach is applied, leading to relevant results on the image.