{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T06:57:17Z","timestamp":1764053837713,"version":"build-2065373602"},"reference-count":157,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T00:00:00Z","timestamp":1676419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"US National Science Foundation","award":["NSF-DMS 1912737","NSF-DMS 1924513","NSF-CCF 1934553"],"award-info":[{"award-number":["NSF-DMS 1912737","NSF-DMS 1924513","NSF-CCF 1934553"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs15041053","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T03:09:21Z","timestamp":1676430561000},"page":"1053","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9482-0258","authenticated-orcid":false,"given":"Sam L.","family":"Polk","sequence":"first","affiliation":[{"name":"Department of Mathematics, Tufts University, 177 College Ave., Medford, MA 02155, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1856-5064","authenticated-orcid":false,"given":"Kangning","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Mathematics, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong"}]},{"given":"Aland H. Y.","family":"Chan","sequence":"additional","affiliation":[{"name":"Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK"},{"name":"Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK"}]},{"given":"David A.","family":"Coomes","sequence":"additional","affiliation":[{"name":"Conservation Research Institute, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK"},{"name":"Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4021-6925","authenticated-orcid":false,"given":"Robert J.","family":"Plemmons","sequence":"additional","affiliation":[{"name":"Departments of Mathematics and Computer Science, Wake Forest University, 1834 Wake Forest Rd., Winston-Salem, NC 27109, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6598-044X","authenticated-orcid":false,"given":"James M.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Tufts University, 177 College Ave., Medford, MA 02155, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Eismann, M.T. 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