# CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering

arXiv: https://arxiv.org/abs/2203.04873 · HTML: https://mlsanity.com/paper/2203.04873/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 51 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Unsupervised Deep Feature Extraction for Remote Sensing Image Classification](https://mlsanity.com/paper/1511.08131/) (2015)
- [AeroRIT: A New Scene for Hyperspectral Image Analysis](https://mlsanity.com/paper/1912.08178/) (2019)
- [Unsupervised Feature Learning by Autoencoder and Prototypical Contrastive Learning for Hyperspectral Classification](https://mlsanity.com/paper/2009.00953/) (2020)
- [Spatial-Spectral Fusion by Combining Deep Learning and Variation Model](https://mlsanity.com/paper/1809.00764/) (2018)

## Built on by (most cited first, 1 of 1)

- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)

## Abstract (arXiv)

Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use patching to incorporate the rich neighborhood information in images and exploit the simplicity and segmentability of the most common HSI datasets. In contrast, most landmasses in the world consist of overlapping and diffused classes, making neighborhood information weaker than what is seen in common HSI datasets. To combat this issue and generalize the segmentation models to more complex and diverse HSI datasets, in this work, we propose our novel flagship model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to combine spectral information extracted from convolutional neural network (CNN) training on a cluster of landscape pixels. Our CEU-Net model outperforms existing state-of-the-art HSI semantic segmentation methods and gets competitive performance with and without patching when compared to baseline models. We highlight CEU-Net's high performance across Botswana, KSC, and Salinas datasets compared to HybridSN and AeroRIT methods.