Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images
This addresses the challenge of material clustering in spectrally-mixed hyperspectral datasets for applications like vegetation classification and environmental monitoring, representing an incremental improvement over existing methods.
The paper tackles the problem of unsupervised material clustering in hyperspectral images, where coarse spatial resolution leads to mixed pixels, by introducing the D-VIC algorithm that incorporates pixel purity to weight single-material pixels, and it outperforms state-of-the-art methods in experiments on datasets like land-use maps and forest health surveys.
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.