# Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry

arXiv: https://arxiv.org/abs/2204.13497 · HTML: https://mlsanity.com/paper/2204.13497/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 8 of 8)

- [Learning by Unsupervised Nonlinear Diffusion](https://mlsanity.com/paper/1810.06702/) (2018)
- [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)
- [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)
- [Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images](https://mlsanity.com/paper/2204.06298/) (2022)
- [Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation](https://mlsanity.com/paper/2203.15619/) (2022)
- [A 3-stage Spectral-spatial Method for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.09294/) (2022)

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

- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests](https://mlsanity.com/paper/2206.09365/) (2022)

## Abstract (arXiv)

Hyperspectral images, which store a hundred or more spectral bands of reflectance, have become an important data source in natural and social sciences. Hyperspectral images are often generated in large quantities at a relatively coarse spatial resolution. As such, unsupervised machine learning algorithms incorporating known structure in hyperspectral imagery are needed to analyze these images automatically. This work introduces the Spatial-Spectral Image Reconstruction and Clustering with Diffusion Geometry (DSIRC) algorithm for partitioning highly mixed hyperspectral images. DSIRC reduces measurement noise through a shape-adaptive reconstruction procedure. In particular, for each pixel, DSIRC locates spectrally correlated pixels within a data-adaptive spatial neighborhood and reconstructs that pixel's spectral signature using those of its neighbors. DSIRC then locates high-density, high-purity pixels far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels and treats these as cluster exemplars, giving each a unique label. Non-modal pixels are assigned the label of their diffusion distance-nearest neighbor of higher density and purity that is already labeled. Strong numerical results indicate that incorporating spatial information through image reconstruction substantially improves the performance of pixel-wise clustering.