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

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.