Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Sam L. Polk; Kangning Cui; Aland H. Y. Chan; David A. Coomes; Robert J. Plemmons; James M. Murphy

doi:10.3390/rs15041053

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arXiv · 2203.09992

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Sam L. Polk·Kangning Cui·Aland H. Y. Chan·David A. Coomes·Robert J. Plemmons·James M. Murphy

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Abstract

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.

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Sam L. Polk, Kangning Cui, Aland H. Y. Chan, David A. Coomes, Robert J. Plemmons, James M. Murphy. 2022-03-18. Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images. https://doi.org/10.3390/rs15041053

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