Open-Pit Mining Area Extraction from High-Resolution Remote Sensing Images Based on EMANet and FC-CRF

Abstract

Rapid and accurate identification of open-pit mining areas is essential for guiding production planning and assessing environmental impact. Remote sensing technology provides an effective means for open-pit mine boundary identification. In this study, an effective method for delineating an open-pit mining area from remote sensing images is proposed, which is based on the deep learning model of the Expectation-Maximizing Attention Network (EMANet) and the fully connected conditional random field (FC-CRF) algorithm. First, ResNet-34 was applied as the backbone network to obtain preliminary features. Second, the EMA mechanism was used to enhance the learning of important information and details in the image. Finally, a postprocessing program based on FC-CRF was introduced to optimize the initial prediction results. Meanwhile, the extraction effect of MobileNetV3, U-Net, fully convolutional network (FCN), and our method were compared on the same data set for the open-pit mining areas. The advantage of the model is verified by the visual graph results, and the accuracy evaluation index based on the confusion matrix calculation. pixel accuracy (PA), mean intersection over union (MIoU), and kappa were 98.09%, 89.48%, and 88.48%, respectively. The evaluation results show that this method effectively identifies open-pit mining areas. It is of practical significance to complete the extraction task of open-pit mining areas accurately and comprehensively, which can be used for production management and environmental protection of open-pit mines.

Open-Pit Granite Mining Area Extraction Using UAV Aerial Images and the Novel GIPNet

“…This involves the crucial task of tracking the extent of mining operations. Such monitoring helps in identifying instances of excessive mining that adversely affect the local environment [4][5][6]. Furthermore, integrating these data with the 3D point cloud information of the mine enables accurate volume calculations [7].…”

“…Xie et al [14] used GaoFen-2 satellite images to create a semantic segmentation dataset for open-pit mines through manual annotation, proposing a UNet-based pixel-level semantic segmentation model. Ren et al [4] introduced a model based on an expectation maximization attention network and a fully connected conditional random field. Xie et al [15] presented DUSegNet, a new network for segmenting open-pit mining areas, which synergizes the strengths of SegNet, UNet, and D-LinkNet, showing competitive performance on GaoFen-2 images.…”