{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T20:26:41Z","timestamp":1783542401418,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T00:00:00Z","timestamp":1647993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China, grant number","award":["(2021YFC3090304)"],"award-info":[{"award-number":["(2021YFC3090304)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, ground penetrating radar is the major technology for the detection of urban road collapses and disaster sources. Vehicle-mounted GPR collects tens of GB of data on site every day, but the present interpretation of abnormal regions detected by radar relies on manual interpretation with low process efficiency. The abnormal region images of GPR are different from the surrounding normal images. In terms of the features of abnormal regions in GPR images with an obvious brightness change and obvious directional characteristics, an abnormal region detection algorithm based on visual attention mechanism is proposed. Firstly, the complex background noise in the GPR images is suppressed by wavelet denoising and gamma transform, and the brightness and directional characteristics of the abnormal regions are enhanced. Secondly, by building a multi-scale image brightness and orientation feature pyramid model, the features of abnormal regions of interest are continuously enhanced, and the rapid screening of abnormal regions has been achieved. The effectiveness of the algorithm has been verified by actual tests on different types of abnormal radar image data.<\/jats:p>","DOI":"10.3390\/rs14071546","type":"journal-article","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T22:08:06Z","timestamp":1648073286000},"page":"1546","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Study on Significance Enhancement Algorithm of Abnormal Features of Urban Road Ground Penetrating Radar Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7383-5592","authenticated-orcid":false,"given":"Fanruo","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Drainage Group Co., Ltd., Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Qiao","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjia","family":"Xing","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.sigpro.2016.05.016","article-title":"An overview of ground-penetrating radar signal processing techniques for road inspections","volume":"132","author":"Benedetto","year":"2017","journal-title":"Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.3390\/rs13132613","article-title":"A GPR-Based Pavement Density Profiler: Operating Principles and Applications","volume":"13","author":"Nectaria","year":"2021","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ndteint.2017.04.002","article-title":"A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis","volume":"96","author":"Lai","year":"2018","journal-title":"NDT E Int."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, J., and Yunling, D. 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