{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T09:54:44Z","timestamp":1779098084645,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"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","award":["2022YFC3003205"],"award-info":[{"award-number":["2022YFC3003205"]}]},{"name":"the National Key Research and Development Program of China","award":["42072306"],"award-info":[{"award-number":["42072306"]}]},{"name":"the National Key Research and Development Program of China","award":["SKLGP2023Z008"],"award-info":[{"award-number":["SKLGP2023Z008"]}]},{"name":"the General Program of the National Natural Science Foundation of China","award":["2022YFC3003205"],"award-info":[{"award-number":["2022YFC3003205"]}]},{"name":"the General Program of the National Natural Science Foundation of China","award":["42072306"],"award-info":[{"award-number":["42072306"]}]},{"name":"the General Program of the National Natural Science Foundation of China","award":["SKLGP2023Z008"],"award-info":[{"award-number":["SKLGP2023Z008"]}]},{"name":"the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["2022YFC3003205"],"award-info":[{"award-number":["2022YFC3003205"]}]},{"name":"the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["42072306"],"award-info":[{"award-number":["42072306"]}]},{"name":"the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project","award":["SKLGP2023Z008"],"award-info":[{"award-number":["SKLGP2023Z008"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5\u201355.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901.<\/jats:p>","DOI":"10.3390\/rs16214075","type":"journal-article","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T12:31:32Z","timestamp":1730377892000},"page":"4075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic Method for Detecting Deformation Cracks in Landslides Based on Multidimensional Information Fusion"],"prefix":"10.3390","volume":"16","author":[{"given":"Bo","family":"Deng","sequence":"first","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1304-4742","authenticated-orcid":false,"given":"Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiujun","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3741-8801","authenticated-orcid":false,"given":"Weile","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingtang","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang Huadong Construction Engineering Co., Ltd., Hangzhou 310000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanzhen","family":"Ju","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiulin","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geo-Hazard Prevention and Geo-Environmental Protection, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes Classification of Landslide Types, an Update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_2","first-page":"1104","article-title":"Research on space-time evolution laws and early warning-prediction of landslides","volume":"27","author":"Xu","year":"2008","journal-title":"Chin. 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