Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

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 a...

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Bibliographic Details
Published in:arXiv.org 2023-02
Main Authors: Polk, Sam L, Cui, Kangning, Chan, Aland H Y, Coomes, David A, Plemmons, Robert J, Murphy, James M
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Density
Diffusion
Discrimination
Hyperspectral imaging
Labels
Maximization
Numerical methods
Optimization
Pixels
Purity
Spatial resolution
Spectral bands
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2203.09992