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Title: 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 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.  more » « less
Award ID(s):
1924513
PAR ID:
10471334
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
MDPI
Date Published:
Journal Name:
Remote Sensing
Volume:
15
Issue:
4
ISSN:
2072-4292
Page Range / eLocation ID:
1053
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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