{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T17:53:48Z","timestamp":1775066028344,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T00:00:00Z","timestamp":1556150400000},"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":["2016YFB0501502; 2016YFA0600302"],"award-info":[{"award-number":["2016YFB0501502; 2016YFA0600302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed approach was employed to extract rare-earth ore mining areas in Dingnan County and Xunwu County, China, using GF-1 (GaoFen No.1 satellite launched by China) and ALOS (Advanced Land Observation Satellite) high-resolution remotely-sensed satellite data, and experimental results showed that FPR (False Positive Rate) and FNR (False Negative Rate) were, respectively, lower than 12.5% and 6.5%, and PA (Pixel Accuracy), MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), and FWIoU (frequency weighted intersection over union) all reached up to 90% in four experiments. Comparison results with traditional classification methods (such as Object-oriented CART (Classification and Regression Tree) and Object-oriented SVM (Support Vector Machine)) indicated the proposed method performed better for object boundary identification. The proposed method could be useful for accurate and automatic information extraction for rare-earth ore mining areas.<\/jats:p>","DOI":"10.3390\/rs11080987","type":"journal-article","created":{"date-parts":[[2019,4,25]],"date-time":"2019-04-25T03:39:02Z","timestamp":1556163542000},"page":"987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-2443","authenticated-orcid":false,"given":"Yan","family":"Peng","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation Hainan Province, Sanya 572029, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Zhaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation Hainan Province, Sanya 572029, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Guojin","family":"He","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation Hainan Province, Sanya 572029, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Mingyue","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Science, Central South University of Forestry and Technology, Changsha 410004, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.coal.2010.11.010","article-title":"Surface Coal Mine Area Monitoring Using Multi-temporal High-resolution Satellite Imagery","volume":"86","author":"Demirel","year":"2011","journal-title":"Int. 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