# Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

arXiv: https://arxiv.org/abs/2203.09992 · HTML: https://mlsanity.com/paper/2203.09992/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 159 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 22 of 22)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Graph Convolutional Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2008.02457/) (2020)
- [Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review](https://mlsanity.com/paper/2001.07307/) (2020)
- [EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral Unmixing](https://mlsanity.com/paper/1708.01894/) (2017)
- [Molecular enhanced sampling with autoencoders: On-the-fly collective variable discovery and accelerated free energy landscape exploration](https://mlsanity.com/paper/1801.00203/) (2017)
- [A Critique of Self-Expressive Deep Subspace Clustering](https://mlsanity.com/paper/2010.03697/) (2020)
- [Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM](https://mlsanity.com/paper/1309.7643/) (2013)
- [Learning by Unsupervised Nonlinear Diffusion](https://mlsanity.com/paper/1810.06702/) (2018)
- [Hierarchical Clustering of Hyperspectral Images using Rank-Two Nonnegative Matrix Factorization](https://mlsanity.com/paper/1310.7441/) (2013)
- [Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders](https://mlsanity.com/paper/1907.08870/) (2019)
- [Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion](https://mlsanity.com/paper/1704.07961/) (2017)
- [Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering](https://mlsanity.com/paper/1902.05402/) (2019)
- [Learning by Active Nonlinear Diffusion](https://mlsanity.com/paper/1905.12989/) (2019)
- [Spatially regularized active diffusion learning for high-dimensional images](https://mlsanity.com/paper/1911.02155/) (2019)
- [A Multiscale Environment for Learning by Diffusion](https://mlsanity.com/paper/2102.00500/) (2021)
- [Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry](https://mlsanity.com/paper/2103.15783/) (2021)
- [Deep Clustering With Intra-class Distance Constraint for Hyperspectral Images](https://mlsanity.com/paper/1904.00562/) (2019)
- [Deep Diffusion Processes for Active Learning of Hyperspectral Images](https://mlsanity.com/paper/2101.03197/) (2021)
- [Unsupervised Classification in Hyperspectral Imagery with Nonlocal Total Variation and Primal-Dual Hybrid Gradient Algorithm](https://mlsanity.com/paper/1604.08182/) (2016)
- [Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data](https://mlsanity.com/paper/2104.13268/) (2021)

## Built on by (most cited first, 4 of 4)

- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [From Orthomosaics to Raw UAV Imagery: Enhancing Palm Detection and Crown-Center Localization](https://mlsanity.com/paper/2509.12400/) (2025)
- [Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging](https://mlsanity.com/paper/2505.00805/) (2025)
- [Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2604.13307/) (2026)

## Abstract (arXiv)

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels that correspond to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets.