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Open AccessArticle

An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images

by Yan Peng 1,2,3, Zhaoming Zhang 1,2,3,*, Guojin He 1,2,3,* and Mingyue Wei 4
1
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
Key Laboratory of Earth Observation Hainan Province, Sanya 572029, China
3
Sanya Institute of Remote Sensing, Sanya 572029, China
4
College of Science, Central South University of Forestry and Technology, Changsha 410004, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 987; https://doi.org/10.3390/rs11080987
Received: 17 February 2019 / Revised: 15 April 2019 / Accepted: 19 April 2019 / Published: 25 April 2019
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. View Full-Text
Keywords: visual attention model; GrabCut; NDVI; mining area; high-resolution remote sensing image visual attention model; GrabCut; NDVI; mining area; high-resolution remote sensing image
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MDPI and ACS Style

Peng, Y.; Zhang, Z.; He, G.; Wei, M. An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images. Remote Sens. 2019, 11, 987.

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