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Hyperspectral images taken from aircraft or satellites contain informationfrom hundreds of spectral bands, within which lie latent lower-dimensionalstructures that can be exploited for classifying vegetation and othermaterials. A disadvantage of working with hyperspectral images is that, due toan inherent trade-off between spectral and spatial resolution, they have arelatively coarse spatial scale, meaning that single pixels may correspond tospatial regions containing multiple materials. This article introduces theDiffusion and Volume maximization-based Image Clustering (D-VIC) algorithm forunsupervised material clustering to address this problem. By directlyincorporating pixel purity into its labeling procedure, D-VIC gives greaterweight to pixels that correspond to a spatial region containing just a singlematerial. D-VIC is shown to outperform comparable state-of-the-art methods inextensive experiments on a range of hyperspectral images, including land-usemaps and highly mixed forest health surveys (in the context of ash diebackdisease), implying that it is well-equipped for unsupervised materialclustering of spectrally-mixed hyperspectral datasets.

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images