Abstract
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.
Highlights
The Rare-earth Ore (REO) mining process, during which topsoil is stripped and large volumes of waste materials are removed from one place to another, leaving huge holes and piles on the Earth’s surface [1], causes continuous change in topography and biodiversity, water pollution, soil erosion, and so on
The mean Intersection over union (MIoU) reached up to 60% when salient regions were used as initial inputs of the original GrabCut method, the MIoU reached up to 90% in all segmentation results using the improved GrabCut method, and the False Positive Rate (FPR) and False Negative Rate (FNR), respectively, were lower than 12.5% and 6.5%
The original GrabCut model can fulfil the entire segmentation, generally using an initial and incomplete user-labelling manually drawn into a rectangle for a natural picture
Summary
The Rare-earth Ore (REO) mining process, during which topsoil is stripped and large volumes of waste materials are removed from one place to another, leaving huge holes and piles on the Earth’s surface [1], causes continuous change in topography and biodiversity, water pollution, soil erosion, and so on These problems have disturbed human life and restricted regional sustainable development, which requires an effective way to monitor and manage the surface mining activities. Object-oriented classification methods, which can use spectral, spatial, textural, and contextual information, were adopted to monitor mining activities with high-resolution satellite images by several researchers [2,7,8]. This method can obtain accurate mining area extraction results, it can be time-consuming, and the process usually depends on manual intervention. Compared with traditional images (e.g., PET image), high-resolution satellite remote sensing images are multi-dimensional and highly complex, Liu et al.’s method must be improved and adapted in order to be applied to high-resolution satellite images
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