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:Remote sensing (Basel, Switzerland) Switzerland), 2023-02, Vol.15 (4), p.1053
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
Ash dieback
Banded structure
Classification
Clustering
Coordinate transformations
Datasets
Dieback
Diffusion
diffusion geometry
Euclidean space
forest health
Graph representations
Hyperspectral imaging
Image classification
Labeling
Land use
Machine learning
Maximization
Mixed forests
Neural networks
Optimization
Pixels
Remote sensing
Satellite imagery
Signal processing
Spatial discrimination
Spatial resolution
Spectral bands
spectral unmixing
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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 corresponding 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:2072-4292
2072-4292
DOI:10.3390/rs15041053