{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:42:29Z","timestamp":1785577349359,"version":"3.56.0"},"reference-count":50,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T00:00:00Z","timestamp":1600560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/NO10523\/1"],"award-info":[{"award-number":["EP\/NO10523\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detection of road pavement cracks is important and needed at an early stage to repair the road and extend its lifetime for maintaining city roads. Cracks are hard to detect from images taken with visible spectrum cameras due to noise and ambiguity with background textures besides the lack of distinct features in cracks. Hyperspectral images are sensitive to surface material changes and their potential for road crack detection is explored here. The key observation is that road cracks reveal the interior material that is different from the worn surface material. A novel asphalt crack index is introduced here as an additional clue that is sensitive to the spectra in the range 450\u2013550 nm. The crack index is computed and found to be strongly correlated with the appearance of fresh asphalt cracks. The new index is then used to differentiate cracks from road surfaces. Several experiments have been made, which confirmed that the proposed index is effective for crack detection. The recall-precision analysis showed an increase in the associated F1-score by an average of 21.37% compared to the VIS2 metric in the literature (a metric used to classify pavement condition from hyperspectral data).<\/jats:p>","DOI":"10.3390\/rs12183084","type":"journal-article","created":{"date-parts":[[2020,9,20]],"date-time":"2020-09-20T21:20:28Z","timestamp":1600636828000},"page":"3084","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Pavement Crack Detection from Hyperspectral Images Using a Novel Asphalt Crack Index"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7641-4723","authenticated-orcid":false,"given":"Mohamed","family":"Abdellatif","sequence":"first","affiliation":[{"name":"School of Civil Engineering, University of Leeds, Woodhouse Lane, Leeds LS9 2JT, UK"},{"name":"School of Computing, University of Leeds, Woodhouse Lane, Leeds LS9 2JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9432-3438","authenticated-orcid":false,"given":"Harriet","family":"Peel","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, University of Leeds, Woodhouse Lane, Leeds LS9 2JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7652-8907","authenticated-orcid":false,"given":"Anthony G.","family":"Cohn","sequence":"additional","affiliation":[{"name":"School of Computing, University of Leeds, Woodhouse Lane, Leeds LS9 2JT, UK"},{"name":"Luzhong Institute of Safety, Environmental Protection Engineering and Materials, Qingdao University of Science &amp; Technology, Zibo 255000, China"},{"name":"School of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao 260061, China"},{"name":"Department of Computer Science and Technology, Tongji University, Shanghai 211985, China"},{"name":"School of Civil Engineering, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8617-7381","authenticated-orcid":false,"given":"Raul","family":"Fuentes","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, University of Leeds, Woodhouse Lane, Leeds LS9 2JT, UK"},{"name":"Departamento de Ingenier\u00eda del Terreno, Universitat Polit\u00e8cnica de Val\u00e8ncia, 46022 Val\u00e8ncia, Spain"},{"name":"Institute of Geotechnical Engineering, RWTH Aachen University, Mies-van-der-Rohe-Stra\u00dfe 1, D52074 Aachen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9628","DOI":"10.3390\/s111009628","article-title":"Adaptive Road Crack Detection System by Pavement Classification","volume":"11","author":"Gavilan","year":"2011","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aldea, E., and Le Hegarat-Mascle, S. 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