{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T16:43:26Z","timestamp":1770137006782,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,26]],"date-time":"2021-12-26T00:00:00Z","timestamp":1640476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFE0200700"],"award-info":[{"award-number":["2020YFE0200700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771397"],"award-info":[{"award-number":["41771397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.<\/jats:p>","DOI":"10.3390\/rs14010103","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"103","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Dongchuan","family":"Yan","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0206-9381","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Observation of Hainan Province, Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572000, China"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xiangqiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}]},{"given":"Hua","family":"Lei","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3553-4849","authenticated-orcid":false,"given":"Kaixuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Lianchong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Fuxiao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Mineral Resources Research, China Metallurgical Geology Bureau, Beijing 101300, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,26]]},"reference":[{"key":"ref_1","first-page":"680","article-title":"Application of analytic hierarchy process to tailings pond safety operation analysis","volume":"29","author":"Wang","year":"2008","journal-title":"Rock Soil Mech."},{"key":"ref_2","unstructured":"Yang, J., Qin, X., Zhang, Z., and Wang, X. 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