Diffusion and Volume Maximization-Based Clustering of Highly Mixed Hyperspectral Images

• URL: http://arxiv.org/abs/2203.09992v1
• Date: Fri, 18 Mar 2022 14:39:42 GMT
• Title: Diffusion and Volume Maximization-Based Clustering of Highly Mixed Hyperspectral Images
• Authors: Sam L. Polk, Kangning Cui, Robert J. Plemmons, and James M. Murphy
• Abstract summary: This article introduces the emphDiffusion and Volume-based Image Clustering (emphD-VIC) algorithm for unsupervised material discrimination. D-VIC locates cluster modes -- high-density, high-purity pixels in the hyperspectral image that are far in diffusion distance from other high-density, high-purity pixels. D-VIC upweights pixels that correspond to a spatial region containing just a single material, yielding more interpretable clusterings.
• Score: 5.16230883032882
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Hyperspectral images of a scene or object are a rich data source, often encoding a hundred or more spectral bands of reflectance at each pixel. Despite being very high-dimensional, these images typically encode latent low-dimensional structure that can be exploited for material discrimination. However, due to an inherent trade-off between spectral and spatial resolution, many hyperspectral images are generated at a coarse spatial scale, and single pixels may correspond to spatial regions containing multiple materials. This article introduces the \emph{Diffusion and Volume maximization-based Image Clustering} (\emph{D-VIC}) algorithm for unsupervised material discrimination. D-VIC locates cluster modes -- high-density, high-purity pixels in the hyperspectral image that are far in diffusion distance (a data-dependent distance metric) from other high-density, high-purity pixels -- and assigns these pixels unique labels, as these points are meant to exemplify underlying material structure. Non-modal pixels are labeled according to their diffusion distance nearest neighbor of higher density and purity that is already labeled. By directly incorporating pixel purity into its modal and non-modal labeling, D-VIC upweights pixels that correspond to a spatial region containing just a single material, yielding more interpretable clusterings. D-VIC is shown to outperform baseline and comparable state-of-the-art methods in extensive numerical experiments on a range of hyperspectral images, implying that it is well-equipped for material discrimination and clustering of these data.

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